Microwave power imaging for ultra-wide band early breast cancer detection

код для вставки на сайт или в блог

ссылки на документ

MICROWAVE POWER IMAGING FOR ULTRA-WIDE BAND EARLY BREAST
CANCER DETECTION
by
Wenyi Shao
A dissertation submitted to the faculty of
The University of North Carolina at Charlotte
in partial fulfillment of the requirements
for the degree of Doctor of Philosophy in
Electrical Engineering
Charlotte
2012
Approved by:
Dr. Ryan S. Adams
Dr. Thomas Weldon
Dr. David Binkley
Dr. Greg Gbur
UMI Number: 3510228
All rights reserved
INFORMATION TO ALL USERS
The quality of this reproduction is dependent on the quality of the copy submitted.
In the unlikely event that the author did not send a complete manuscript
and there are missing pages, these will be noted. Also, if material had to be removed,
a note will indicate the deletion.
UMI 3510228
Copyright 2012 by ProQuest LLC.
All rights reserved. This edition of the work is protected against
unauthorized copying under Title 17, United States Code.
ProQuest LLC.
789 East Eisenhower Parkway
P.O. Box 1346
Ann Arbor, MI 48106 - 1346
UMI Number: 3510228
All rights reserved
INFORMATION TO ALL USERS
The quality of this reproduction is dependent on the quality of the copy submitted.
In the unlikely event that the author did not send a complete manuscript
and there are missing pages, these will be noted. Also, if material had to be removed,
a note will indicate the deletion.
UMI 3510228
Copyright 2012 by ProQuest LLC.
All rights reserved. This edition of the work is protected against
unauthorized copying under Title 17, United States Code.
ProQuest LLC.
789 East Eisenhower Parkway
P.O. Box 1346
Ann Arbor, MI 48106 - 1346
ii
c 2012
Wenyi Shao
ALL RIGHTS RESERVED
iii ABSTRACT
WENYI SHAO. Microwave power imaging for ultra-wide band for
early breast cancer detection. (Under the direction of DR. RYAN S. ADAMS)
Due to the critical need for complementary or/and alternative modalities to current Xray mammography for early-stage breast cancer detection, a 3D active microwave
imaging system has been developed. This thesis presents a detailed method for rapid,
high contrast microwave imaging for the purpose of breast survey. In the proposed
imaging system, several transmitters polarized in different directions take turns sending
out a low-power UWB pulse into the breast; backscattered signals are recorded by a
synthetic aperture antenna array. These backscattered signals are passed through a
beamformer, which spatially focuses the waveforms to image backscattered energy as a
function of location in the breast. A simple Delay-and-Sum algorithm is applied to test
the proposed multistatic multi-polarized detection scheme. The obtained 2-D and 3-D
numerical results have demonstrated the feasibility and superiority of detecting small
malignant breast tumors using our antenna strategy. An improved algorithm of
microwave power imaging for detecting small breast tumors within an MRI-derived
phantom is also introduced. Our imaging results demonstrate that a high-quality image
can be reached without solving the inverse problem.
To set up an experimental system for future clinical investigation, we developed two
Vivaldi antennas, which have a notable broad band property, good radiation pattern, and
a suitable size for breast cancer detection. Finally, an antenna array which consists of
eight proposed Vivaldi antennas is introduced. By conveniently moving up/down and
iv rotating this antenna array, it can be used for the multistatic breast cancer imaging and
qualified for our multi-polarized scan mode.
v ACKNOWLEDGMENTS
It was quite a journey to purse the Ph. D., and I have too many people to thank. Not
exaggerating, I would not have been able to complete this journey without their support
over the past several years.
I must first express my gratitude towards my advisors, Professor Ryan S. Adams. He
not only helped me keep growing technically and professionally, but his leadership,
vision, and strategic thinking also set an example I hope to match someday.
I wish to thank those faculties and technical staff at UNCC for their consulting and
assistance in technical areas: Thomas Weldon, David Binkley, Greg Gbur, and Pat
Winter.
I would like to thank the graduate students I have worked with on different projects in
UNCC Woodward Hall 143: Aaron Hatley, Tsz Kim Lai, and Joshua Shehan. They each
helped make my time in the Ph.D. program more fun and interesting. I look forward to
future collaboration with any of them.
Last but not least, millions of thanks to my family: my wife and my parents, who have
been supporting every decision I made over the years.
vi TABLE OF CONTENTS
LIST OF TABLE
viii
LIST OF FIGURES
ix
CHAPTER 1: INTRODUCTION
1
1.1
Background and motivation
1
1.2
Dielectric properties of breast tissues
3
1.3
Active microwave imaging for breast cancer detection
8
1.4
Objects and outline
CHAPTER 2: FDTD-BASED SIMULATION IN THE BREAST MODEL
10
12
2.1
Finite Difference Equations
12
2.2
Absorbing boundary conditions and stability
18
2.3
2-D FDTD simulation
24
2.4
3-D FDTD simulation
30
CHAPTER 3: MICROWAVE IMAGING VIA SIMPLIFIED BREASTMODELS
33
3.1
Antenna array and signal calibration
33
3.2
Delay and Sum
36
3.3
Hemispherical breast model
39
3.4
Image reconstruction with multi-polarized signals
45
3.4.1
Single target detection
45
3.4.2
Dual target and resolution
48
CHAPTER 4: ADVANCED MICROWAVE IMAGING VIA MICROWAVE
POWER IMAGING (MPI)
4.1
Fields generated by a linearly-polarized dipole
53
53
vii 4.2
MPI and multi-polarized MPI
61
4.3
The MRI-derived breast phantom and FDTD simulation
65
4.4
Imaging results using the MRI-derived breast phantom
70
4.4.1
Single tumor
70
4.4.2
Dual tumor
74
CHAPTER 5: UWB ANTENNA DESIGN AND FABRICATION
78
5.1
Two antipodal Vivaldi antennas
79
5.2
The antenna array
85
CHAPTER 6: CONCLUSIONS
89
BIBLIOGRAPHY
91
APPENDIX A: INTRODUCTION TO COLE-COLE EQUATION
100
viii LIST OF TABLES
TABLE 2.1: Dielectric properties for each part in Fig. 2.7.
25
TABLE 3.1: Nominal dielectric properties of breast tissues.
40
TABLE 4.1: Comparison of SMR and SCR value for Fig. 4.12, 4.13, and 4.14.
74
TABLE 5.1: The coefficients used in equation for building the antenna.
79
ix LIST OF FIGURES
FIGURE 1.1: Debye curve fits of measured baseline dielectric-property data.
4
FIGURE 1.2: Percent tissue type as a function of dielectric constant.
5
FIGURE 1.3: Specimens for dielectric properties over 20 GHz bandwidth.
6
FIGURE 1.4: Comparison of median Cole-Cole curves for normal and malignant
tissue.
7
FIGURE 1.5: A cylindrical breast model and the reconstructed image.
9
FIGURE 2.1: Yee cell in FDTD.
14
FIGURE 2.2: A waveguider-like model to test the absorbing boundary.
20
FIGURE 2.3: Wave propagates in the modelI.
22
FIGURE 2.4: Wave propagates in the model II.
23
FIGURE 2.5: 2-D FDTD simulation for a simplified breast model.
25
FIGURE 2.6: The detecting UWB signal in time domain and frequency domain.
26
FIGURE 2.7: Electric Field evolves within the computation region over time.
27
FIGURE 2.8: A rectangular breast model for microwave breast cancer imaging.
31
FIGURE 2.9: Signals obtained from 4 receivers.
32
FIGURE 3.1: The antenna array for detecting tumor in the rectangular breast model.
34
FIGURE 3.2: A self-symmetrical antenna array in Klemm’s experiment.
35
FIGURE 3.3: Signal calibration.
36
FIGURE 3.4: The reconstructed image in section planes.
38
FIGURE 3.5: The simplified inhomogeneous hemispherical breast model.
40
FIGURE 3.6: Section views of the inhomogeneous breast model.
41
FIGURE 3.7: Spectrum of the UWB signal for the hemispherical model detection.
42
x FIGURE 3.8: 3-D image of a 6 mm in diameter tumor obtained via DAS method.
43
FIGURE 3.9: The Cross sections of the reconstructed image.
44
FIGURE 3.10: The multi-polarization scheme for the multistatic imaging.
46
FIGURE 3.11: The reconstructed images using multi-polarized scheme.
47
FIGURE 3.12: The elevation angle affects the resolution.
49
FIGURE 3.13: Reconstructed image for 2 targets 13 mm apart with
multi-polarization.
50
FIGURE 3.14: Reconstructed image for 2 targets 11 mm apart with
multi-polarization.
51
FIGURE 3.15: Reconstructed image for 2 targets 13 mm apart with
single-excitation.
52
FIGURE 3.16: Reconstructed image for 2 targets 11 mm apart with
single-excitation.
52
FIGURE 4.1: Observe the field generated by a electric dipole.
54
FIGURE 4.2: The Ey field generated by the dipole source.
55
FIGURE 4.3: The Ex field generated by the dipole source.
56
FIGURE 4.4: The Ez field generated by the dipole source.
57
FIGURE 4.5: The Hy field generated by the dipole source.
58
FIGURE 4.6: The Hx field generated by the dipole source.
59
FIGURE 4.7: The Hz field generated by the dipole source.
60
FIGURE 4.8: The product of Ey field and Hz field at a given receiver.
63
FIGURE 4.9: Distribution of dielectric properties of an MRI-derived breast phantom. 66
FIGURE 4.10: Dielectric distribution of Yee cell within the breast phantom.
67
FIGURE 4.11: 56 receivers lie along 7 circles for doing breast survey.
68
FIGURE 4.12: The reconstructed image through multi-polarized MPI method.
71
xi FIGURE 4.13: The reconstructed image through multi-polarized DMAS method.
72
FIGURE 4.14: The reconstructed image through multi-polarized DAS method.
73
FIGURE 4.15: Reconstructed image using ±15% random-difference tumor-free
breast phantom.
75
FIGURE 4.16: Two-tumor prototype for study of horizontal resolution.
76
FIGURE 4.17: Two-tumor prototype for study of vertical resolution.
77
FIGURE 5.1: Geometry and parameters of a proposed Vivaldi antenna.
80
FIGURE 5.2: Geometry and parameters of a second Vivaldi antenna.
81
FIGURE 5.3: The constructed Vivaldi antenna #1.
82
FIGURE 5.4: The constructed Vivaldi antenna #2.
82
FIGURE 5.5: The 2-D Electric field of Antenna #1 in the x-y plane.
83
FIGURE 5.6: The 2-D Electric field of Antenna #2 in the x-y plane.
84
FIGURE 5.7: The designed antenna array in HFSS.
84
FIGURE 5.8: The designed antenna array for breast cancer imaging.
85
FIGURE 5.9: The constructed antenna array.
86
FIGURE 5.10: The designed antenna array for breast cancer imaging.
86
FIGURE 5.11: The constructed antenna array. (a) top view, (b) bottom view.
88
CHAPTER 1: INTRODUCTION
1.1 Background and motivation
Breast cancer is the most common form of cancer among women. In 2005, an
estimated 211,240 new cases of invasive breast cancer were diagnosed among women
in the U.S., as well as an estimated 58,490 additional cases of in situ breast cancer [1].
In 2007, the estimated number of new breast cancer is markedly lower than the
estimate for 2005 due to the use of new, more accurate estimation methods. However,
thereafter the number has increased year by year. An estimated 230,480 new cases of
invasive breast cancer will be diagnosed among women in 2011, as well as an estimated
57,650 additional cases of in situ breast cancer [2]. In addition, appromimately 40,000
women are expected to die from breast cancer each year since 2005. According to a
new survey commissioned by the Society for Womens Health Research, breast cancer
has been the disease American women fear the most [3].
Naturally, diagnosing breast cancer early can help women receive treatment early,
so they have an increased chance of survival. Many techniques have been developed
to use for breast cancer detection:
X-ray mammography is currently the leading method for detecting this type of
cancer. Unfortunately, this method is fraught with problems such as high false negative rates [4] and high false positive rates [5]. Additionally, the ionizing nature of
X-rays poses a considerable risk of causing the very cancer it attempts to detect.
Moreover, there are minor problems such as: since X-ray mammography is a 2-D projection imaging technique, breast compression is required to create a uniform volume
between the flat-source board and the flat-receiver board. This makes patients feel
very uncomfortable.
2
Magnetic resonance imaging (MRI) is assumed to be the most accurate and ideal
imaging approach. It was even believed by some experts that this technique had
the potential to distinguish malignant tumor from the glandular tissues, which is
much denser than any other healthy breast tissues. However, a recent report shows
that MRI is too sensitive to detect early-stage breast cancer, which might lead to
unnecessary surgery [6]. Moreover, an MRI scan is generally very expensive. Additionally, there are relatively few MRI centers, especially outside of major cities. These
drawbacks make MRI less suitable for routine breast cancer screening.
Ultrasound is used to determine whether a lesion detected on a mammogram is
a liquid cyst or a solid tumor, but is not able to provide additional information.
Other imaging methods, including thermography and electrical impedance imaging,
are not appropriate for early breast detection because of limitations in image quality
and diagnosis accuracy. Therefore, a new safe, low-cost, reliable complement to the
X-ray mammography with high image contrast and resolution for early breast cancer
detection is neccessary. Microwave detection has the potential to satisfy this need.
However, several issues must be solved before microwave imaging is equipped for
practical use. For instance, are the methods applied in RADAR imaging or ground
penetrating imaging suitable for microwave medical examination? Is the current
screening approach and strategy sufficient? Secondly, loss in tissues tends to increase
with frequency, so generally the frequency is limited to approximately 10 GHz. This
begs the question: how much bandwidth is enough to generate sufficient resolution
for breast cancer detection, and what resolution can it reach with less than 10 GHz
bandwidth?... This dissertation aims to answer questions like these, and contribute
to the microwave medical breast cancer detection community.
3
1.2 Dielectric properties of breast tissues
The physical basis of microwave imaging for breast cancer detection is the dielectricproperty contrast between normal and malignant tissue in the microwave spectrum.
This section briefly reviews several published dielectric measurements on breast tissues at microwave-frequencies.
From 1984 to 1994, a few studies have been conducted on the dielectric properties of cancerous and healthy breast tissues at microwave frequencies. For instance,
Chaudhary et al. [7] measured the dielectric properties of excised healthy and malignant breast tissue specimens from 3 MHz-3 GHz in 1984. 4 years later, Surowiec et
al. [8] published the measurement of dielectric properties of infiltrating breast carcinoma and selected surrounding nonmalignant tissue in the range of 20 kHz-100 MHz.
In 1994, Joines et al. [9] measured the dielectric properties of freshly excised tissues
from several organs, including breasts, over the frequency range 50-900 MHz. These
three studies have consistently shown that the dielectric constant and conductivity
for cancerous breast tissue is three or more times greater than that of the host tissue.
These studies are summarized in Fig. 1.1. These data suggest a contrast between
malignant and healthy breast tissue of approximately 5:1 in dielectric constant and
10:1 in conductivity in the microwave frequency range. Fig. 1.1 also shows that the
comparison of permittivity and conductivity between normal and malignant tissue
continues over 3 GHz, based on the single-pole Debye equation.
Further extrapolation and results regarding dielectric properties of normal breast
tissue and cancerous tissue were presented in 2007 bby Lazebnik and her colleagues
[11] [14]. Rather than discuss “healthy“ breast tissue simply as in previous investigations, Lazebnik et al. measured composition of adipose, glandular and fibroconnective tissue at microwave frequency. It was found that both the dielectric constant and
conductivity tend to decrease as the adipose content increases, and conversely as the
percent glandular and/or fibroconnective tissue increases, both the dielectric constant
4
Figure 1.1: Single-pole Debye curve fits of measured baseline dielectric-properties
data for normal and malignant breast tissue at radio and microwave frequencies [10].
and conductivity increase [11]. This trend is consistent over a wide frequency band
from 0.5 GHz to 20 GHz.
The difference in dielectric properties arise essentially due to the large differences
in water content of breast tissues (adipose, glandular and fibroconnective tissue). As
the adipose content increases, the water content is reduced, corresponding to the
reduced microwave dielectric properties. Fig. 1.2 shows how the dielectric properties
change as the tissue composition of normal breast tissue changes. Note that when
the adipose content nears zero, (i.e. the tissue is only composed of glandular and
fibroconnective tissue), the dielectric constant and conductivity have reached the level
of malignant tissue indicated in Fig. 1.1. However, there is still a large dielectricproperty contrast between the normal adipose-dominated tissues and the malignant
tissues in the breast. Reference [14] implies a nearly 10:1 contrast between malignant
5
Figure 1.2: Percent tissue type as a function of dielectric constant for (a), and effective
conductivity for (b) at 5GHz [14].
tissue and normal tissue that is almost entirely adipose. Although these data were
measured at 5 GHz, similar trends were observed at 10 and 15GHz.
Therefore, the performance of “normal” breast tissue may be different due to its
composition. Fig. 1.3 gives a better illustration of the change of dielectric properties
over a wide frequency band. The curves are color coded based on the adipose content
of each sample. In the order of highest to lowest adipose content, the colors are red,
purple, blue, cyan and green. This implies that for those with low-adipose content,
breasts are not very transparent to microwave. And it might be difficult to recognize
malignant tumors in these dense breasts with microwave energy.
To solve this problem, one feasible solution is to classify the breasts according to
their density, and treat each class differently. To design numerical breast phantoms
with dispersion models that are suitable for computational electromagnetics simulations of micowave imaging and cancer detection, Lazebnik et. al. classified all of
their specimens of breast tissue into 3 groups based on the percent adipose tissue in
each sample. Group 1 contained all samples with 0 − 30% adipose tissue (the highwater-content group); group 2 contained all samples with 31 − 84% adipose tissue,
6
Figure 1.3: 85 normal data specimens for dielectric constant and conductivity over 20
GHz bandwidth. The solid black curve represents the dielectric properties of saline,
the dashed black curve represents the dielectric properties of lipids, and the dash-dot
black curve represents the dielectric properties of blood [14].
and group 3 contained all samples with 85 − 100% adipose tissue (the low-watercontent group). Median dielectric constant and conductivity dispersion curves were
obtained for each group and finally concluded in Fig. 1.4. Solid lines in Fig. 1.4 (a)
and (b) stand for three adiposed-defined normal tissue groups obtained from cancer
surgeries. In the order of highest to lowest dielectric properties are group 1, group 2
and group 3. Dashed lines are median Cole-Cole curves [Appendix] [12] [13] for group
2 for nomal tissue samples obtained from reduction surgeries. Fig. 1.4 (c) and (d) are
median Cole-Cole curves for the dielectric constant and conductivity respectively, of
cancer samples with minimum malignant tissue content of 30%. Note that the curve
of group 1 (from very dense breast) is close to that of the cancerous sample (as mentioned in [14], contrast between malignant and fibroconnective/glandular-dominated
tissue is 610%). While group 2 and group 3 still have a significant comparative
difference from cancerous tissue.
Thus, the dielectric property contrast at microwave frequencies appears to be
more significant than the few-percent contrast exploited by X-rays, especially for
7
Figure 1.4: Comparison of median Cole-Cole curves for normal and malignant tissue.
(a) and (b) are dielectric constant and conductivity, respectively, for 3 groups normal
tissue (solid lines). (c) and (d) are dielectric constant and conductivity, respectively, of
cancer samples. Symbols stand for measured dielectric properties for tissue-mimicking
phantom materials (*, 10% oil; O, 30% oil; /, 50% oil; , 80% oil) [15].
breasts that are not very dense (like group 2 and group 3 in Lazebnik’s experiment).
Actually, when assisted with contrast agents, microwave detection is even able to
provide a good image for very dense breasts, in which the contrast agents modify
the dieletric properties of malignant tissue to increase the dielectric contrast with
fibroglandular tissue [16] [17].
8
1.3 Active microwave imaging for breast cancer detection
To date, two main types of active microwave breast imaging techniques have been
proposed: hybrid microwave-induced acoustic imaging, and radar-based microwave
imaging (non-hybrid). In the hybrid mode [19]− [25], microwave signals are transmitted into the breast to heat the tumor - due to the difference of water content, a
tumor absorbs more heat than normal breast tissues - and ularasound transducers
are used to detect pressure waves generated by the heated tumor.
In the non-hybrid method, the breast is first illuminated by a microwave signal,
and the scattered microwave signals are processed to form an image of the cancerous region. Due to the difference in dielectric properties inroduced in Section
1.2, an inverse scattering method that constructs images by recovering the permittivity or conductivity profile of the breast is an intuitive approach to detect cancerous growth. Examples of such methods are diffraction tomographic (DT) algorithm [26], Born approximation (BA) [27], Born iterative method (BIM) [28] and
distorted BIM (DBIM) [29]. These methods were originally developed for ground
penetrating RADAR (GPR), but some of these, and other similar inverse methods,
have been effectively applied in the area of breast cancer medical imaging [30]− [35].
However, these methods are generally time-consuming for processing 3-D images, especially when a relatively large number of iterations are involved to obtain an accurate
image.
In 2001 and thereafter, Hagness et al proposed a simple, rapid but effective approach for the microwave detection of breast cancer [10] [36] [37]. This approach only
focuses on reflections from scatterers but avoids estimating the dielectric properties
of the entire area. The essence of this approach is Delay-And-Sum (DAS). As the
illuminating signal is an ultra-wideband pulse, this translates to simply time shifting
and summing signals. Fig. 1.5(a) shows the cylindrical model Hagness et al used for
studying the DAS method. A cylinder, covered with a thin skin layer, was assummed
9
(a)
(b)
Figure 1.5: (a) The cylindrical model and the monostatic antenna system to detect a
buried malignant tumor; (b) the reconstructed image for (a) in the x-y plane where
the tumor exists [37].
to contain healthy breast tissue (r = 9, σ = 0.4S/m, random variation up to ±10%)
and a small malignant tumor (r = 50, σ = 4S/m). Antennas are settled in 9 rows and
each acts as a transmitter and a receiver. Fig. 1.5(b) shows the reconstructed image
in the x-y cross-plane in which the tumor exists. Although the breast model in [37]
looks very simple today, it illustrated a new way in which microwave medical imaging
can be accomplished easiliy, rapidly and efficiently. A more developed DAS method
– improved-DAS (IDAS) [38], is an extension of DAS. It uses an additional weight
factor that essentially represents the preprocessing and coherent radar operation, calculated at each focal point to improve image quality. The Delay-Multipy-and-Sum
(DMAS) is another appoach in the DAS family, in which the time-shifted signals are
multiplied in pair before summing [39]. DAS and its follow-up algorithms have shown
to be very efficient approaches with acceptable image contrast and resolution.
Furthermore, many other beamforming approaches have been presented to localize
the breast tumor. Typical examples are space-time beamforming [40]− [42], robust
capon beamforming (RCB) [43], FDTD-based time reversal (FBTR) [44]− [46], gen-
10
eralized likelihood ratio test [47], adaptive [48] and multistatic adaptive microwave
imaging (MAMI) [49] [50]. These methods have contributed to microwave breast cancer diagnosis research, and proved that the active microwave method is an effective
complement to the current techniques for breast cancer detection.
To apply the approaches described above, researchers have designed a variety of
antennas to excite and receive UWB probing signals. The trend of UWB antennas is
to make them small, and able be moved/rotated conveniently throughout the scan.
A review of UWB antennas for medical applications is made in Chapter 5.
1.4 Objects and outline
This dissertation proposes an improved scheme for UWB microwave imaging for
small breast tumor detection. The primary goals of this research include:
- characterization of UWB signals propagating in the breast tissue and scattering
from cancerous tissue employed by the finite-difference-time-domain (FDTD) method;
- development and evaluation of imaging algorithms for detecting early (small)
breast tumors;
- design and measurement of an UWB antenna and antenna array;
- some conclusions and suggestions for the clinical experiment system setup.
In this dissertation, a multistatic antenna system is employed to detect a small
tumor with diameter less than 1 cm in a simple breast model (Chapter 3) and an
MRI-derived breast phantom (Chapter 4), respectively. The transmitter sends out
a short-pulse signal into the breast and the backscatters are recorded by several receivers. The backscattered signals are then processed similar to a beamformer, to
image the backscattered energy as a function of the locations in the breast. Chapter 2 introduces the behavior of programmed FDTD which simulates the process
of detecting signal excitation, propagation, and backscattered signal collection. In
Chapter 3, the DAS approach is applied to image a simple breast model through the
data extracted from the FDTD simulation of Charpter 2. Chapter 4 proposes a new
11
algorithm that is similar to DAS. Tested by imaging an MRI-derived breast phantom, this new algorithm is able to produce a better-quality image than any previous
DAS-family approach, but keeps the advantage of DAS-family’s efficiency. Chapter
5 presents a new UWB antenna and an antenna array for the breast cancer detection
measurement. And the final conclusion is made in Chapter 6.
CHAPTER 2: FDTD-BASED SIMULATION OF A BREAST MODEL
The finite-difference time-domain (FDTD) formulation of electromagnetic simulation is a convenient tool for solving scattering problems. This method, first introduced
by Yee in 1966 [51] and later developed by Taflove and others [52]- [55], is a direct
solution of Maxwell’s time-dependent curl equations. This scheme treats the irradiation of the scatterer as an initial value problem. In this chapter, discussion of the
FDTD method will cover:
* finite difference equations;
* absorbing boundary conditions and stability;
* 2-D field FDTD example;
* 3-D field FDTD example.
Note that the contents discussed in this chapter is related to, and based on the
topic of breast tissue/model applied in this dissertation .
2.1 Finite Difference Equations
In an isotropic medium, Maxwell’s equations can be written
5 × E = −µ
∂H
∂t
5 × H = σE + (2.1)
∂E
∂t
(2.2)
The vector Eqn (2.1) and (2.2) represent a six scalar equation series, which can
be expressed in Cartesian coordinates as:
13
∂Hx
1 ∂Ey ∂Ez
=
−
∂t
µ ∂z
∂y
∂Hy
1 ∂Ez ∂Ex
=
−
∂t
µ ∂x
∂z
∂Hz
1 ∂Ex ∂Ey
=
−
∂t
µ ∂y
∂x
1 ∂Hz ∂Hy
∂Ex
=
−
− σEx
∂t
∂y
∂z
∂Ey
1 ∂Hx ∂Hz
=
−
− σEy
∂t
∂z
∂x
1 ∂Hy ∂Hx
∂Ez
=
−
− σEz
∂t
∂x
∂y
(2.3)
(2.4)
(2.5)
(2.6)
(2.7)
(2.8)
Following Yee’s approach, these differential equations can be approximated with difference equations if one positions the components of E and H throughout a unit cell
of the lattice as shown in Fig. 2.1.
We take Eqn. (2.6) as an example and write its equivalent difference equation:
Exn+1 (i + 21 , j, k) − Exn (i + 12 , j, k)
∆t
n+1/2
n+1/2
1
Hz
(i + 2 , j + 12 , k) − Hz
(i + 12 , j − 12 , k)
=
∆y
n+1/2
−
Hy
n+1/2
(i + 12 , j, k + 12 ) − Hy
∆z
(i + 12 , j, k − 21 )
1
− σExn+1/2 (i + , j, k)) (2.9)
2
where, n + 1/2 represents the time instant t = (n + 1/2)∆t. If the approximation
E n+1 (i + 12 , j, k) + Exn (i + 12 , j, k)
1
Exn+1/2 (i + , j, k) = x
2
2
is made, Eqn. (2.9) can be rearranged as
(2.10)
14
Figure 2.1: Yee cell in FDTD. [51]
1
Exn+1 (i + , j, k)
2
1
=CA(m) · Exn (i + , j, k)
2
" n+1/2
n+1/2
(i + 21 , j + 12 , k) − Hz
(i + 12 , j − 12 , k)
Hx
+ CB(m) ·
∆y
#
n+1/2
n+1/2
(i + 12 , j, k − 12 )
Hy
(i + 12 , j, k + 12 ) − Hy
−
∆z
where
CA(m) =
CB(m) =
(m)
∆t
(m)
∆t
−
+
σ(m)
2
σ(m)
2
1
(m)
∆t
+
σ(m)
2
σ(m)∆t
2(m)
σ(m)∆t
2(m)
(2.12)
∆t
(m)
+ σ(m)∆t
2(m)
(2.13)
1−
=
=
1+
1
(2.11)
where m = (i + 1/2, j + 1/2, k + 1/2), i.e. the center of the Yee cell. Similarly, the
difference equations of Eqn. (2.7) and Eqn. (2.8) may be written as
15
1
Eyn+1 (i, j + , k)
2
1
=CA(m) · Eyn (i, j + , k)
2
" n+1/2
n+1/2
Hx
(i, j + 12 , k + 21 ) − Hx
(i, j + 12 , k − 21 )
+ CB(m) ·
∆z
#
n+1/2
n+1/2
Hz
(i + 12 , j + 12 , k) − Hz
(i − 21 , j + 12 , k)
−
∆x
(2.14)
and
1
Ezn+1 (i, j, k + )
2
1
=CA(m) · Ezn (i, j, k + )
" n+1/2 2
n+1/2
Hy
(i + 12 , j, k + 12 ) − Hy
(i − 12 , j, k + 21 )
+ CB(m) ·
∆x
#
n+1/2
n+1/2
(i, j + 12 , k + 21 ) − Hx
Hx
(i, j − 21 , k + 12 )
−
∆y
(2.15)
Eqn.(2.11), (2.14), and (2.15) are computations of electric field in FDTD over time
and space. Similarly, at any instant of t = n∆t, we can obtain the equations for H
field computaion:
1
1
Hxn+1/2 (i, j + , k + )
2
2
1
1
=CP (m) · Hxn−1/2 (i, j + , k + )
2
2
n
Ez (i, j + 1, k + 21 ) − Ezn (i, j, k + 21 )
− CQ(m) ·
∆y
#
1
n
n
Ey (i, j + 2 , k + 1) − Ey (i, j + 21 , k)
−
∆z
(2.16)
16
1
1
Hyn+1/2 (i + , j, k + )
2
2
1
1
=CP (m) · Hyn−1/2 (i + , j, k + )
2
2
n
Ex (i + 12 , j, k + 1) − Exn (i + 12 , j, k)
− CQ(m) ·
∆z
1
n
n
Ez (i + 1, j, k + 2 ) − Ez (i, j, k + 12 )
−
∆x
(2.17)
1
1
Hzn+1/2 (i + , j + , k)
2
2
1
=CP (m) · Hzn−1/2 (i + , j +
2
"
n
Ey (i + 1, j +
− CQ(m) ·
1
, k)
2
1
, k) − Eyn (i, j + 21 , k)
2
∆x
Exn (i + 21 , j + 1, k) − Exn (i + 12 , j, k)
−
∆y
(2.18)
where
CP (m) =
CQ(m) =
µ
− σm2(m)
∆t
µ(m)
+ σm2(m)
∆t
1
µ(m)
∆t
+
σm (m)
2
(2.19)
(2.20)
In our simulation, we assume that the magnetic conductivity, σm = 0 everywhere in
the breast. And for high frequency, µ = µ0 , hence CP (m) and CQ(m) are simplified
to
CP (m) = 1
CQ(m) =
∆t
µ0
(2.21)
(2.22)
For convenience, we will use identical grid spacing in the x, y, and z directions, i.e.
∆x = ∆y = ∆z = δ. Hence, Eqn. (2.11) and Eqn.(2.14) − Eqn.(2.18) can be
arranged:
17
1
Exn+1 i + , j, k
2
1
=CA(m) · Exn i + , j, k
2
1
1
1
1
n+1/2
0
n+1/2
i + ,j − ,k
+ CB (m) Hz
i + , j + , k − Hz
2
2
2
2
1
1
1
1
+ Hyn+1/2 i + , j, k −
−Hyn+1/2 i + , j, k +
2
2
2
2
(2.23)
1
Eyn+1 i, j + , k
2
1
=CA(m) · Eyn i, j + , k
2
1
1
1
1
0
n+1/2
n+1/2
+ CB (m) Hx
i, j + , k +
− Hx
i, j + , k −
2
2
2
2
1
1
1
1
−Hzn+1/2 i + , j + , k + Hzn+1/2 i − , j + , k
2
2
2
2
(2.24)
Ezn+1 i, j, k +
1
2
1
=CA(m) · Ezn i, j, k +
2
1
1
1
1
0
n+1/2
n+1/2
+ CB (m) Hy
i + , j, k +
− Hy
i − , j, k +
2
2
2
2
1
1
1
1
−Hxn+1/2 i, j + , k +
+ Hxn+1/2 i, j − , k +
2
2
2
2
(2.25)
1
1
i, j + , k +
2
2
1
1
0
n
n
+ CQ (m) · Ey i, j + , k + 1 − Ey i, j + , k
2
2
1
1
−Ezn i, j + 1, k +
+ Ezn i, j, k +
(2.26)
2
2
1
1
1
1
n−1/2
n+1/2
i + , j, k +
=Hy
Hy
i + , j, k +
2
2
2
2
1
1
n
0
n
+ CQ (m) · Ez i + 1, j, k +
− Ez i, j, k +
2
2
1
1
−Exn i + , j, k + 1 + Exn i + , j, k
(2.27)
2
2
Hxn+1/2
1
1
i, j + , k +
2
2
=Hxn−1/2
18
1
1
Hzn+1/2 i + , j + , k
2
2
1
1
=Hzn−1/2 i + , j + , k
2
2
1
1
n
0
n
+ CQ (m) · Ex i + , j + 1, k − Ex i + , j, k
2
2
1
1
(2.28)
−Eyn i + 1, j + , k + Eyn i, j + , k
2
2
where
CQ0 (m) =
CQ(m)
∆t
=
δ
µ0 δ
(2.29)
Hence, Eqn.(2.23) − (2.28), and Eqn.(2.12), (2.13), and 2.29 are the final difference
equations that will be applied in our FDTD simulation.
2.2 Absorbing boundary conditions and stability
An artificial termination is necessary to truncate the solution region electrically
close to the radiating/scattering object but effectively simulate the solution to infinity.
These termination conditions, known as absorbing boundary conditions (ABCs), are
theoretically able to absorb incident and scattered fields. The accuracy of the ABC
dictates the accuracy of the simulation. Reference [56] − [65] have listed various types
of ABCs, among which, the perfectly matched layer (PML) [60]− [64] is probably the
most accurate approach. However, the reason PML is not applied in our simulation is
that PML generally consumes too much computation resources, such that a simulation
with PML absorbing boundary usually takes an unreasonably long time. Considering
the efficiency, we use Liao’s ABCs [57] [58], instead.
Liao’s absorbing boundary conditions are an efficient tool and have shown to yield
excellent results using double-precision arithmetic when the angle of incidence is not
too large [66]. We will apply Liao’s ABCs in our simulation but before this, an
evaluation will be made to understand the accuracy of this boundary condition.
Let E(t, x1 ) (electric field, or H if magnetic field) denote the wave incident on the
boundary point x1 at time t. Then, using Liao’s boundary condition, we have
19
E(t + ∆t, x1 ) ≈
N
X
(−1)j+1 CjN Tj Ej
(2.30)
j=1
where CjN is the binomial coefficient and N is the order of the binomial coefficient.
The vector Ej is defined as


 E1,j 


 E2,j 


Ej = 

 ... 




E2j+1,j
(2.31)
where






Ei,j = E(tj , xi )
tj = t − (j − 1)∆t




xi = x1 − (i − 1)∆x
(2.32)
For 2nd order Liao’s ABC, i.e., N = 2, we obtain
E(t + ∆t, x1 ) ≈ 2T1 E1 − T2 E2
(2.33)
The interpolation vector, T1 and T2 are defined as follows:


0
T1 0



T2 = T1 · 
0
T
0
1




0
0 T1
(2.34)
and
T1 =
T11 T12 T13
(2.35)
20
where


T11 = (2 − s)(1 − s)/2






T12 = s(2 − s)








(2.36)
T13 = s(s − 1)/2
s = v∆t/∆x
where v is the propagatation speed of the wave. Eqn.(2.33) indicates that if the 2nd
order Liao’s ABC is applied, we need to record the data of the current-step electric
field and the previous-step electric field for the next step computation. T1 and T2 are
parameters of current-step electric field and previous-step electric field, respectively
for the computation, and can be obtained through Eqn. (2.34) − Eqn. (2.36).
Figure 2.2: A waveguider-like model to test the absorbing boundary.
To evaluate the result of Liao’s 2nd order ABC, we set up a long-retangular box
that has a length of 500 Yee cells in the z direction and 20 × 20 cells in the x-y plane,
as shown in Fig. 2.2. This box is bounded with perfect electric conductor (PEC) on
the upper and lower surfaces, and perfect magnetic conductor (PMC) on the front
and back. A plane wave signal, sent from the source plane in the middle of the
box, and absorbed by the absorbing boundary at the terminals, is a simple Gaussian
21
pulse. Assuming that the upper limit of the frequency is 10 GHz, this translates
to 3cm-long wavelength in free space, and 1cm wavelength in typical breast tissue
(relative permitivity =9). Thus, a grid spacing of the Yee cell of 1mm × 1mm × 1mm
is sufficient to provide accuracy. Consider the stability condition [54] [67], the time
step is required to satisfy the equation:
δ
∆t ≤ √ ≈ 1.9 × 10−12 second
c 3
Hence, we select ∆t =
5
ps,
3
i.e.,
5
3
(2.37)
× 10−12 s. If this box is filled with air (or
vacuum), according to Eqn. (2.36), then we have s = 0.5. Fig. 2.3 despicts several
instants of propagation in the model, terminated by Liao’s 2nd order ABC, using the
FDTD method. In Fig. 2.3(a), the Gaussian pulse is generated in the center position
of the model; (b) shows the generated pulse starts to move in the +z and -z direction,
respectively; (c) shows the pulses continuing to propagate in the model; (d) shows the
pulses impinging upon the boundary; (e) indicates that there is a reflection from the
boundary, meanwhile, most of the pulse energy has “gone out” of the model through
the boundary; (f) shows the reflected wave propagating back, and its amplitude drops
to approximately 2 × 10−5 (-94 dB). It is interesting to note that the reflected pulse
is distorted, instead of the simple Gaussian pulse originarily generated. Fig. 2.3
demonstrates that the absorption of Liao’s 2nd order ABC from normal incidence in
air is quite good.
As a second test, assume that the box shown in Fig. 2.2 is filled with a particular
medium (r = 9, σ = 0), which has similar dielectric constant to that of typical
breast tissue. Using the same δ and ∆t as above, we can obtain s = 0.167, and
corresponding T1 , T2 through Eqn. (2.34) − Eqn. (2.36). Fig. 2.4 shows the
signal propagating in the medium-filled model, using FDTD and Liao’s 2nd order
ABC. Fig.2.4(a) shows the Gaussian pulse forming; (b) indicates that the pulse has
22
been completely generated and starts propagating toward both two terminals; (c)
shows the signal propagating in the medium-filled box; (d) shows the microwave
signal is impinging upon the absorbing boundary; in Fig. 2.4(e), the signal vibrates
dramatically because of the combination of the Gausian-pulse’s tail and the reflected
wave; (f) shows the reflected wave heading back. Again, the shape of reflected wave
is no longer the original Gaussian pulse but distorted. We do not know why this
happens, but its maximum value is reduced to about 2.5 × 10−5 (-92 dB), which is
what we expected to see.
(a)
(b)
(c)
(d)
Figure 2.3: Wave propagates in the model shown in Fig.2.2. (a) 100th step, (b) 150th
step, (c) 400th step, (d) 560th step.
23
(e)
(f)
Figure 2.3: Wave propagates in the model shown in Fig.2.2: (e) 600th, (f) 700th step.
(a)
(b)
(c)
(d)
Figure 2.4: Wave propagates in the model shown in Fig.2.2 filled with medium. (a)
80th step, (b) 210th step, (c) 480th step, (d) 1400th step.
24
(e)
(f)
Figure 2.4: Wave propagates in the model shown in Fig.2.2 filled with medium. (e)
1460th step, (f) 1970th step.
To summrize, we have demonstrated that the 2nd order Liao’s ABC is able to absorb most of the energy of the incident wave. Meanwhile, it yields a −94dB reflection
in air, and a −92dB reflected wave if in a specific medium with r = 9, σ = 0. This
slight reflection is negligible in our FDTD simulation for the breast caner detection
investigation.
2.3 2-D FDTD simulation
Our purpose is to simulate microwave signal propagation and scattering in the
breast. Before we extend it to 3-D, a 2-D model is introduced in order to easily
explain our scheme. In Fig. 2.5, the 2-D space is split into several parts. The skin
layer is located from Y = 75mm to Y = 77mm, below which is the breast region and
above that is the air. The black spot buried in the breast region represents a tumor.
The excitation source and the receivers are located in the air, on the surface of the
skin. The dielectric parameters for each part is displayed in Table 2.1. Since the
dielectric difference of the air and skin is very large, we assume that our excitation
and receivers are positioned on the surface of the skin tightly to prevent most of the
signal energy from reflecting on the surface of the skin but not enter the breast region.
25
Figure 2.5: 2-D FDTD simulation for a simplified breast model.
The absorbing boundaries have been designed for each region (air, skin, and breast
region) that contacts the boundary.
Table 2.1: Dielectric properties for each part in Fig. 2.5
Tissues
air
Skin
Breast Tissue
Tumor
Relative Permittivity (F/m) Conductivity (S/m)
1
0
36
4
9
0.4
50
4
The TM mode is applied in our 2-D FDTD simulation. Therefore, only Hx , Hy ,
and Ez are used in the FDTD equations and only 3 difference equations (Eqn. (2.38)
− (2.40) are employed in the computation. Note that the absorbing boundaries are
H-field boundaries. The excitation signal is a modulated Gaussian pulse, which is
shown in the time-domain and frequency domain, respectively, in Fig. 2.6(a) and (b).
26
(a)
(b)
Figure 2.6: The detecting UWB signal in (a) time domain, and (b) in frequency
domain.
1
1
=CA(m) ·
i − ,j +
2
2
1
1
0
n
n
+ CB (m) Hy i, j +
− Hy i − 1, j +
2
2
1
1
−Hxn i − , j + 1 + Hxn i − , j
(2.38)
2
2
1
1
n+1
n
Hx
i − , j + 1 =Hx i − , j + 1
2
2
1
3
1
1
n+ 12
n+ 12
0
i − ,j +
− Ez
i − ,j +
+ CQ (m) · Ez
2
2
2
2
Ezn+1/2
1
1
i − ,j +
2
2
Ezn−1/2
(2.39)
Hyn+1 i, j +
1
2
1
1
1
n+ 12
n
0
=Hy i, j +
+ CQ (m) · Ez
i + ,j +
2
2
2
1
1
n+ 1
−Ez 2 i − , j +
2
2
(2.40)
The 3dB bandwidth of the signal is approximately 5 GHz, while the peak of the
spectrum appears near 3.2 GHz. Assuming that the excitation is positioned at x =
60mm, y = 77mm in Fig. 2.5, and 4 receivers are positioned at (x = 20mm, y =
77mm), (x = 40mm, y = 77mm), (x = 80mm, y = 77mm), and (x = 100mm, y =
77mm) respectively. Fig. 2.7 records the electric field Ez at several time steps.
27
Fig. 2.7(a) shows the source starts to radiate at step t = 100∆t. (b) represents the
excitation is strengthening. In (c), the excitation has reached its maximum, which is
corresponding to the time instant t = 400ps in Fig.2.6(a). (d) and (e) denote that
the radiated pulse penetrates through the skin layer and goes deeper into the breast
tissue area. Note that the wave speed in air is much faster than in the skin and the
breast, therefore, a wavefront in the air has already met the top boundary, when the
wavefront in the breast is still propagating toward the tumor. (f) and (g) show that
the wave is passing through the tumor. It can be seen that the field in the area close
to x = 60mm, y = 60mm, which is the center of the tumor, is much stronger than
elsewhere, resulting from the field response of the tumor. Fig. 2.7(h)-(k) denote that
the wave propagates deeper into the breast, and has arrived the left and right boundary in (k). At this point, it is worth noting that the scattered field of the tumor is
combined with the incident field, and cannot be discerned in these figures, since the
scattered field is much weaker than the incident field. In Fig. 2.7(l)-(n), the process
of wave absorption by the bottom absorbing boundary is depicted.
(a) 100th step
(b) 180th step
Figure 2.7: Electric Field evolves within the computation region over time.
28
(c) 240th step
(d) 280th step
(e) 310th step
(f) 340th step
(g) 360th step
(h) 400th step
Figure 2.7: continued figure, electric Field evolves within the computation region over
time.
29
(i) 440th step
(j) 500th step
(k) 570th step
(l) 650th step
(m) 710th step
(n) 810th step
Figure 2.7: continued figure, electric Field evolves within the computation region over
time.
30
The entire process of simulation contains 1000 time steps and this takes less than
2 minutes. The signals recorded at 4 watching points (on the surface of skin layer)
in the time domain converges very well. The success of 2-D simulation indicates
that FDTD is able to imitate the process of transmission and receiving of microwave
signals, and to provide effective data for breast cancer detection. In the next section,
we will extend this simulation to 3D, with the same scheme described in this section.
2.4 3-D FDTD simulation
3-D FDTD is much more complicated, memory-consuming, and time-consuming
than 2-D simulation. In this section, we develop a simple 3-D model for our FDTD
simulation. Assumming that the grid size is still ∆x = ∆y = ∆z = 1mm, and
the time step remains ∆t =
5
ps.
3
The computation model, which approximates a
compressed breast, is illustrated in Fig. 2.8.
A muscle layer has been added at the bottom of the model, which is 25 mm thick
in the z direction. It has similar dielectric properties (r = 50, σ = 4.0) to that of
the tumor. A spherical tumor, shown in red in Fig. 2.8, is 25mm beneath the skin
layer. A transmitter, prepared to send out a UWB microwave pulse, is highlighted
in green on the surface of the skin; Twenty-five receivers to collect the response from
the tumor are placed on the surface of the skin. Above the skin is an air layer which
is 30mm thick.
Since the rectangular model has six faces, the absorbing boundaries are designed
to fit each part of the breast model. For the left, right, front, and rear faces, the
absorbing boundaries are split into several layers to fit each layer in the breast model
(muscle layer, breast tissue layer, skin layer and air layer).
The total computation volume is 120mm × 120mm × 107mm. One simulation,
containing 2000 time steps to allow the detecting signal transmission and the backscattered signals received at the antenna array, takes approximately 30 minutes on our
linux computation server. Fig. 2.9 illustrates 4 selected signals obtained by 4 re-
31
Figure 2.8: A rectangular breast model for microwave breast cancer imaging.
ceivers, respectively, of the collecting-antenna-array. Note that the signals collected
by the receivers on the surface of the skin, are composed of the incident wave from
the excitation source, the reflections from the interface between the skin and breast
tissue, and the tumor response. Thus, a calibration step is required to extract the
tumor response and will be discussed in the next chapter.
32
(a) Antenna No.1
(b) Antenna No.2
(c) Antenna No.3
(d) Antenna No.4
Figure 2.9: Signals obtained from 4 receivers.
CHAPTER 3: MICROWAVE IMAGING VIA SIMPLIFIED BREAST MODELS
In this chapter, two simplied breast models are employed to attempt detection
of a malignant tumor, using the delay-and-sum(DAS) algorithm. The idea of multipolarization detection will also be introduced in this chapter.
3.1 Antenna array and signal calibration
To detect a tumor growth in the breast, the simplified rectangular breast model
shown in Fig. 2.8 described in section 2.4, is used in this section, as well as the UWB
probing pulse introduced in Fig. 2.6. The antenna array utilized in our investigation,
consisting of 25 elements, is shown in Fig. 3.1. The separation between the elements
is 20mm in the x direction as well as in the y direction, and is less than the Nyquist
sampling space for the pulse employed. Each element in the array is an electrically
small dipole antenna (an advanced fabricated UWB antenna will be discussed in
Chapter 6). Our 5 × 5 array creates an 80mm × 80mm synthetic aperture in the
horizontal plane at z = 77mm. Theoretically, the cross-resolution is proportional to
the size of the synthetic aperture, i.e. the bigger the synthetic aperture the higher
the cross resolution. However, the equation for calculating far-field resolution for
synthetic aperture radar may not be suitable for near-field imaging. Therefore, we
will use a numerical method to investigate the resolution problem. This will be
discussed later in this chapter.
A simulated scan involves 25 independent signals recorded by the antenna array.
These stored waveforms include the incident signal, skin reflection, and the tumor
response. To isolate tumor reponse, a calibration process is required to remove the
artifacts.
34
Figure 3.1: The antenna array for detecting tumor in the rectangular breast model.
There are two basic types of calibration methods: experimental method, and
signal-processing method. A typical example of the experimental method is reported
in [38], in which Klemm et al. proposed a symmetical antenna array to remove
the undesired signal. Fig. 3.2 illustrates the symmetrical curved antenna array for
microwave breast cancer detection applied in Klemm’s experiment. By physically
rotating the antenna array around its center, two or more radar measurements are
performed. In these sets of measured data, undesired signals (skin reflection and
mechanical-part reflection) are supposed to be identical and appear at the same time
position, so that they can be eliminated. However, this method faces many limitations: (i) distance between antennas and skin must remain unchanged, (ii) skin properties and thickness must remain the same, and (iii) healthy breast tissue properties
must be homogenous. The signal-processing method to remove unwanted artifacts
can be quite complicated, such as in [68], where an algorithm based on multiplication
of adjacent wavelet subbands is applied to enhance the tumor response while reducing
the skin reflection. The skin responses are finally distinguished and eliminated in the
35
Figure 3.2: A self-symmetrical antenna array for microwave radar breast cancer detection in Klemm’s experiment [38].
wavelet domain using an artificial threshold. Some relatively simple signal-processing
methods, such as subtraction from the averaged skin reflection (see in [37]), are not
necessarily able to yield the result in real world scenarios as desired. Therefore, to
date, artifact removal is still a problem that needs to be overcome in the microwave
breast-cancer-detection community.
In our investigation, the tumor response is extracted by subtracting a reference
model, which is an indentical but tumor-free model from the tumor-included model.
Since a regular tomography examination is recommended at least once each year [69],
the previous exmamination data can be reasonably used as reference data. Since
tissue properties may vary over time, in our investigation the breast model containing
a tumor has ±10% random variation (in dielectric constant as well as conductivity for
each cell) from the tumor-free model. In Fig. 3.3(a), the blue dashed curve represents
36
(a)
(b)
Figure 3.3: Signal calibration. (a) signal obtianed by an antenna in a tumorfree/tumor-bearing model. (b) Tumor response, after the subtraction.
a signal obtained from a single receiver in a tumor-free model, while the red solid curve
represents a signal obtained from the same receiver in a tumor-bearing model. In this
figure, a notable difference appears only over a specific short time window, which is
assumed to be the echo of the tumor. The calibrated signal, after subtraction, is
illustrated in Fig. 3.3(b), which represents the tumor response. This result suggests
that the tumor response is much weaker than the undesired signals, compared to
the waveforms shown in 3.3(a). This method, using a priori information, though
not perfect, helps us to easily obtain reliable tumor-response data for performance
analysis of the imaging approach.
3.2 Delay and Sum
The DAS method, first proposed for breast cancer detection [10] [37], was designed
for a monostatic detecting system. In this system, a UWB antenna is used as a
transmitter as well as the receiver and is moved across the breast to form a virtual
synthetic aperture. The tumor-backscatter signals received by all antennas, are timeshifted (phase-shifted) before they are summed to form a synthetic focal point. The
37
th
th
m time-delay, needed to compensate (shift) the m antenna for a given focal point,
is given by
→
2dm (−
r)
→
Tm (−
r)=
v
(3.1)
→
where dm (−
r ) represents the distance between the focal point and the mth transmit−
ter/receiver element located at position →
r , and v represents the average velocity of
propagation in the breast at the center frequency of the pulse. In our multistatic
detecting system, time delays from the transmitter to the target point are identical
for any received signal, since only one transmitter is employed. Therefore, the timedelay required for compensation only contains the propagation from the target point
to the receiver. Thus, the time-delay compensation in our multistactic system should
be rewritten as
→
dm (−
r)
→
Tm (−
r)=
v
(3.2)
where (1 6 m 6 25). A multistatic system avoids the issue that the transmitting
channel and the receiving channel must be highly isolated as in a monostatic system,
since the echo of the tumor is extremely weak when compared with the transmitted
microwave signal.
Finally, the summation of all time-shifted signals form the intensity of a pixel in
the reconstructed image. This process can be expressed by
→
I(−
r)=
"
25
X
#2
→
S(Tm (−
r ))
(3.3)
m=1
Note that in some applicaitons of the DAS algorithm, there is a weight term
used to compensate the attenuation for each signal propagating in the breast. This
compensation term was not used in our investigation since this artificial adjustment
may cause additional undesired effects. Additionally, previous investigations have
38
shown that an attenuation-compensated summation does not convincingly provide a
better image over non-attenuation-compensated summation.
(a)
(b)
(c)
Figure 3.4: The reconstructed image (a) in the x − y plane, (b) in the z − x plane,
and (c) in the z − y plane. (unit: mm).
Since the signals add coherently at the target point and incoherently everywhere
else, intensity at the target area is much stronger than any other location in the breast
region. A focal-point scan throughout the breast was carried out in increments of 1
39
mm in x, y, as well as z directions in 3-D space. The reconstructed images are
shown in Fig. 3.4. The red spot located at (50, 50) in Fig. 3.4(a) is the precise
location of a 6-mm-diameter tumor in the FDTD model. Note that the suppression
of clutter signals in the surrounding regions is quite good. This result demonstrates
that the simulation data generated from our FDTD code are correct and effective.
Some visible bright spots around the tumor represent leakages and can be reduced
by increasing the number of antennas used in the array, or by image post-processing.
The profile of the tumor in the coronal plane (z − x plane) and in the sagittal plane
(z − y plane) is distorted, due to poor resolution in the z direction, resulting from no
synthetic aperture in the z direction. In the next section, we will use a hemispherical
breast model so a synthetic aperture can be formed in the z direction as well.
3.3 Hemispherical breast model
A hemispherical breast model is clearly more practical than a retangular model. In
this section, a hemispherical model, shown in Fig. 3.5, is developed for the microwave
breast cancer imaging.
In Fig. 3.5, a hemisphere with 100mm diameter, centered at (70,70,25), filled
with breast tissues, and covered with a layer of 2mm-thick skin, is positioned above a
25mm thick muscle layer (the muscle layer is shown in Fig. 3.6(a)). The black point
in Fig. 3.6(a) and (b), embedded in the normal tissue represents a 6mm diameter
malignant tumor. The dielectric properties of the fatty breast tissues are assumed to
be Gaussian random variables with variations of ±10% around their nominal values.
The nominal values are chosen to be typical of the reported data [7] − [9] [11] − [15], as
summarized in Table 3.1. The glandular tissue is the main source of clutters because
their dielectric properties have an upper bound very close to those of malignant tumors
[11] [14]. The size of the glandular tissue ranges from 1 to 5 mm in diameter randomly
and their locations are also random in the generated breast model. The randomly
40
Figure 3.5: The simplified inhomogeneous hemispherical breast model viewed in 3D.
distributed breast tissues with variable dielectric properties are representative of the
inhomogeneity of the breast of an actual patient.
Table 3.1: Nominal dielectric properties of breast tissues
Tissues
Immersion liquid
Muscle
Skin
Fatty Breast Tissue
Glandular Tissue
Tumor
Permittivity (F/m) Conductivity (S/m)
9
0
50
4
36
4
9
0.4
10-45
0.4-3.6
50
4
A synthetic aperture antenna array, consisting of 32 elements (black points around
the model), is positioned around the breast model in four layers as shown in Fig. 3.5,
and each layer has 8 elements. Each element is assumed to be a dipole antenna and
41
(a)
(b)
Figure 3.6: Slice of the breast model. (a) coronal slice at y = 70mm; b) transverse
slice at z = 50mm.
is positionad 2mm away from the surface of the skin. The dot on the top of the
breast model shown in Fig. 3.5 represents the transmitter. To reduce the microwave
reflection on the surface of the skin, allowing more signal energy to enter the breast,
the breast model as well as the antenna array are assumed to be immersed in a
coupling medium [70] [71], which has a similar dielectric constant to the breast fatty
tissue. This design helps to reduce the impedance mismatch on the surface of the
skin, and simplify the calculation of time delay in the DAS algorithm.
To obtain good resolution, a UWB pulse signal having wider bandwidth than the
one previously described is employed in this program. Its time domain expression is
a modulated Gaussian pulse:
" 2 #
t − t0
V (t) = sin(2πf t)exp −
τ
(3.4)
42
Figure 3.7: Spectrum of the UWB signal for the hemispherical model detection.
where f = 6.25GHz, τ = 50ps, and t0 = 4τ . This pulse is centered around 6.25
GHz and has a full-width at half-maximum (FWHM) bandwidth of approximately
10 GHz, which is shown in Fig. 3.7.
The whole computation region, including the breast model, the antenna array,
and the coupling medium, is 140mm × 140mm × 107mm. The grid size ∆x = ∆y =
∆z = 1mm, and time step ∆t = 35 ps are used in this 3-D FDTD simulation. The
computaion time for one transmission and the backscattered signals to be received at
the antenna array is approximately 40 minutes (2000 steps) on our linux server.
The DAS algorithm is used here to obtain a reconstructed image. Fig. 3.8 shows
the 3-D images obtained via DAS method. The shaded hemisphere represents the
contour of the breast, and the dotted shades inside correspond to the intensity of
the backscattered energy estimates. The image is displayed on a logarithmic scale
with a 3 dB dynamic range (focal points with backscattered energy lower than -3 dB
43
Figure 3.8: 3-D image of a 6 mm in diameter tumor obtained via DAS method. The
dotted shades inside the breast are the intensity of the backscattered energy estimates.
are removed). The tumor is conspicuously shown in the correct location defined in
FDTD with visible clutters around it. Fig. 3.9 shows the reconstructed image in x-y
(transverse), x-z (coronal), and y-z (sagittal) cross section images with all the focal
points on display. The tumor is clearly located at (50,70,50), in the cartesian coordinate from these sliced images. The resolution in the vertical direction (z-direction) is
much better than in the retangular model due to the geometry of the array. There is
a strong clutter near the tumor, which may make it difficult to distinguish the profile
and stage of the tumor in real diagnosis.
44
(a)
(b)
(c)
Figure 3.9: The Cross sections of the reconstructed image. (a) in transverse plane,
(b) in coronal plane, and (c) in sagittal plane.
Note that our multistatic scheme is different from the multistatic method introduced in some articles, such as in [39] [49] [50], where each antenna in the array takes
turn to transmit a probing pulse and all antennas in the array are used to receive
45
the backscattered signals. That approach is equivalent to our simulation repeated N
times, where N is the number of antennas in the array. For instance, in reference [50],
64 antennas were used which implies 64 × 64 series of signal are summed in the beamforming image reconstruction. Therefore, the final reconstructed images are naturally
different than those presented here. Since the approach in [50] may take a long time
to complete the simulation, we developed a multi-polarized method, in which several
transmitters, instead of all antennas in the array, are used and each takes its turn
to transmit a probing pulse. Relatively speaking, our enhanced method is able to
improve the quality of the reconstructed image and save simulation (or microwave
scan) time.
3.4 Image reconstruction with multi-polarized signals
In this section, we develop an enhanced method to image the backscattered-energy
distribution within the hemispherical breast model. This method is able to improve
the image quality over the results obtained in the previous section.
3.4.1 Single target detection
Naturally, a specific polarized signal brings specific information, and dictates the
resolution in a certain direction. Thus, we attempt to use two kinds of linearlypolarized electric signals as a probing signal to illuminate the breast model. In Fig.
3.10, four dipole antennas positioned around the breast model shown in red arrows,
are polarized in the +y, +x, −y, and −x direction respectively. Each transmitter is
4mm away from the surface of the skin and sends out a probing pulse, after a previous
transmission by another excitation and collection by the array. The locations of the
excitation antennas have been moved from the top to the sides of the model, to
allow better imaging of tumors growing near the skin. The array used to collect the
backscattered signals remains unchanged. The probing pulse in each transmission is
the 10 GHz bandwidth signal, shown in Fig. 3.7, which was employed in the previous
investigation. Since each excitation and collection is an independent event, multi-
46
Figure 3.10: The multi-polarization scheme for the multistatic imaging.
core simultaneous computation, with each core dealing with an independent FDTD
simulation, is applied in our scheme. Therefore, the computaional time for the entire
scheme to complete, is approximately equivalent to a single-excitation simulation.
The signal calibration step and time-delay compensation are processed similar to
the previous section. Consequently, the intensity of each focal point is summed by a
group of four backscattered signals and each has 32 series, i.e.
→
I(−
r)=
"
32
X
#2
"
→
S+y (Tm (−
r ))
+
m=1
"
+
32
X
m=1
32
X
#2
→
S+x (Tm (−
r ))
m=1
#2
→
S−y (Tm (−
r ))
"
+
32
X
m=1
#2
→
S−x (Tm (−
r ))
(3.5)
47
(a)
(b)
(c)
Figure 3.11: The reconstructed images using multi-polarized scheme shown in Fig.
3.10. a) in transverse plane, (b) in coronal plane, and (c) in sagittal plane.
The reconstructed images are shown in Fig. 3.11. Compared to Fig. 3.9, the
clutters in each counterpart of Fig. 3.11 have experienced significantly improved
48
suppression. The highlighted dot in the figure which stands for the mapped energy
of a tumor, is easily distinguished from the background [72].
This scheme provides better resolution in the horizontal plane (x-y-plane image
such as Fig. 3.11(a)) than any single-polarized detection with probing signal polarized
in the x-y plane. The polarization of the probing signal in our investigation does
not take z direction into account due to the radiation of a dipole antenna. In the
remainder of this section, we will investigate the resolution problem of the proposed
approach.
3.4.2 Dual target and resolution
Resolution is an important reference index to evaluate an imaging system. Since
microwave medical imaging is a type of near-field imaging, the equation proposed
for radar-imaging resolution may not be appropriate for near-field medical imaging.
However, the resolution of near-field imaging does follow some basic criteria.
Generally, range-resolution is determined by the equation [73]
δ=
v
2B
(3.6)
where, v is the speed of wave propagation, and B is the bandwidth of the detecting
signal. Therefore, high range-resolution is obtained when a wide-band detecting signal
is applied. This is the reason UWB signals are employed for breast cancer detection.
The cross-resolution is assummed to be proportional to the elevation angle θ,
depicted in Fig. 3.12(a), i.e., the bigger the elevation angle, the higher the crossresolution. Therefore, cross-resolution is limited in the rectangular breast model due
to its geometry and the structure of the antenna array, shown in Fig. 3.12(a) (or 3-D
model in Fig. 2.8). On the other hand, the array used for the hemispherical model
forms a large elevation angle, shown in Fig. 3.12(b), which is able to yield a high
49
(a)
(b)
Figure 3.12: The elevation angle affects the resolution. The elevation angle in (a) is
smaller than in (b), which implies a relatively poor image resoltution.
cross-resolution. Meanwhile, the resolution in z-direction can be improved since a
synthetic aperture is also formed in the z-direction.
In this subsection, a dual-tumor case [74] is used to investigate resolution of the
imaging method proposed within this chapter. In general, imaging resolution is defined by the minimal distance between two targets at which two targets can still be
distinguished in the reconstructed image. Typically, it can be characterized by a -3
dB drop of the power level in the reconstructed image. Since resolution of the hemispherical system is superior to the rectangular system, in this section, the horizontal
(x-y plane) resolution is investigated only through the hemispherical breast model.
Moreover, due to the symmetry of the sphere, it is difficult to distinguish the definition of range-resolution and cross-resolution in the hemispherical model, therefore a
discussion of z-direction resolution is ommited.
A pair of spherical targets with identical diameter of 6 mm, were positioned in
the center of the breast model. The hemispherical breast models with and without
tumors differ with ±10% random variation. Using the antenna scheme depicted in
Fig. 3.10, we try to distinguish both of the targets with minimal distance between
them. Fig. 3.13 shows the reconstructed image with multi-polarized DAS method
when two targets are placed 13 mm apart in the FDTD simulation. Two targets
50
Figure 3.13: Reconstructed image for a pair of targets with 13 mm apart using multipolarized DAS method.
can be distinguished well in this case but with slight distortion of the location and
poor contrast. As the center distance moves to 11 mm, a very poor-contrast image
is obtained, shown in Fig. 3.14. Two targets cannot be distinguished clearly for this
case, and some focal points between two tumors have scattered energy that exceed
-3 dB of the peak tumor response. Additionally, many strong clutters are present in
this image and some of their scatterd energy have reached the level of the tumor. It
turns out that to distinguish the tumor from the clutters is difficult with this image.
To summarize, the resolution of the proposed imaging system, accompanied with
the DAS image reconstruction algorithm, is approximately 13 mm. Meanwhile, this
conclusion demonstrates that microwave breast cancer imaging is a type of centimeterresolution detection.
51
Figure 3.14: Reconstructed image for a pair of targets with 11 mm apart using multipolarized DAS method.
To compare with the multi-polarized method, we also investigated using a singleexcitation strategy with the antenna array shown in Fig. 3.5 to detect dual targets.
However, nothing can be concluded from these figures, whether the distance between
two targets is 13 mm or 11 mm. The reconstructed images are shown in Fig. 3.15
and Fig. 3.16. The actual tumor locations merely exbihit extremely weak-bright dots,
and clutters are even stronger than the targets.
In this chapter, we have demonstrated the advantage of the multi-polarized antenna strategy. The DAS algorithm is a simple, effective approach and has certain
robustness, for detecting an early-stage breast tumor. However, a real human-breast
can be highly heterogeneous, which means its dielectric properties may be more inhomogeneous than the models we developed in this chapter. In the next chapter, we
will propose a more advanced algorithm to treat an MRI-derived breast phantom,
which exhibits the dielectric properties of the human breast tissues more realistically.
52
Figure 3.15: Reconstructed image for a pair of targets with 13 mm apart using singleexcitation strategy.
Figure 3.16: Reconstructed image for a pair of targets with 11 mm apart using singleexcitation strategy.
CHAPTER 4: ADVANCED MICROWAVE IMAGING VIA MICROWAVE
POWER IMAGING (MPI)
The inhomogeneity of the dielectric constant of breast tissues cause variation in
wave velocities propagating in them. For near-field imaging such as a breast survey,
propagation of the wave is restricted to a short distance, therefore, small variations
of the time delay (typically 6 10%) will not cause significant impact on the image
reconstruction (a review of this result can be found in [75]). However, for highly
heterogeneous breasts, the complexity of wave propagation in the breast increases,
and the simple DAS method faces difficulties in distinguishing the cancerous part
within a breast over the clutters, principally induced from fibroconnective tissue or
glandular tissue. In this chapter, a new type of confocal microwave imaging method
- microwave power imaging algorithm, is proposed to image a spherical tumor within
an MRI-derived breast phantom, which is more complicated, but more realistic than
models discussed in previous chapters.
4.1 Fields generated by a linearly-polarized dipole
To clearly describe the algorithm, it is necessary to understand each component
of the field (in Cartesian coordinates) generated by a linearly-polarized dipole source.
We position an electric dipole, linearly polarized in the y-direction, in the center
of the computation region (140 mm× 140 mm× 107 mm, in the air) and use the 3-D
FDTD method to observe the generated electric field and magnetic field in three crosssection planes, shown in Fig. 4.1. Note that what we care about is the wavefront,
i.e., the propagation of the phase, rather than field amplitude.
Figs. 4.2 −4.7 show the Ey , Ex , Ez , Hy , Hx , and Hz field in three section-plane,
respectively, after a modulated-Gaussian pulse is fully generated. From these figures,
54
Figure 4.1: Observe the field generated by a electric dipole in three cross-section plane:
horizontal plane (x-y plane with z=50), coronal plane (z-x plane with y=60), and
sagittal plane (z-y plane with x=60). The center position of the dipole is (70,70,55),
in the cartesian coordinate.
it can be concluded that the wavefront (i.e. phase) of the Ey field, generated by the
y-direction-linearly-polarized dipole, is spherically symmetric in space; the wavefront
of Ex and Ez is spherically anti-symmetric about a 45◦ -tilted plane, respectively. The
wavefront of Hx is spherically anti-symmetric about a horizontal plane in which the
dipole is located; the wavefront of Hz is spherically anti-symmetric about a vertical
plane in which the dipole is located. And nothing can be concluded about Hy from
the figure.
Similarly, the scattered field generated by a scatter that is illuminated by a transmit field should have the same response since the scatter is the source of a scattered
field. The DAS algorithm similar to the one employed in Chapter 3 is described
as follows: if the excitation antenna is linearly-polarized in the y-direction, the collected Ey -backscattered signals can be summed coherently at each target location
55
(a)
(b)
Figure 4.2: The Ey field generated by the dipole source,
observed in (a) horizontal
plane, (b) coronal plane, and
(c) sagittal plane, respectively
shown in Fig 4.1.
(c)
56
(a)
(b)
Figure 4.3: The Ex field generated by the dipole source, observed in (a) horizontal plane,
(b) coronal plane, and (c)
sagittal plane, respectively,
shown in Fig. 4.1.
(c)
57
(a)
(b)
Figure 4.4: The Ez field generated by the dipole source, observed in (a) horizontal plane,
(b) coronal plane, and (c)
sagittal plane, respectively,
shown in Fig .4.1.
(c)
58
(a)
(b)
Figure 4.5: The Hy field generated by the dipole source, observed in (a) horizontal plane,
(b) coronal plane, and (c)
sagittal plane, respectively,
shown in Fig. 4.1.
(c)
59
(a)
(b)
Figure 4.6: The Hx field generated by the dipole source, observed in (a) horizontal plane,
(b) coronal plane, and (c)
sagittal plane, respectively,
shown in 4.1.
(c)
60
(a)
(b)
Figure 4.7: The Hz field generated by the dipole source, observed in (a) horizontal plane,
(b) coronal plane, and (c)
sagittal plane, respectively,
shown in Fig. 4.1.
(c)
61
after time-delay compensation, through Eqn. 3.3, because of the spherical symmetry
of Ey . However, other electric and magnetic components are not able to reconstruct
an image merely using Eq. 3.3, due to their spatial asymmetry.
Our improved algorithm aims at applying both the electric-field signal and the
magnetic-field signal, and combining them to achieve more information from the target, in order to reconstruct a high-contrast image. Dipole antennas are employed
throughout our investigation. However, advanced UWB antenna design and fabrication will be discussed in Chapter 6. In the next section, the proposed algorithm will
be described in detail.
4.2 MPI and multi-polarized MPI
It is desired to improve the efficacy of the multistatic confocal microwave imaging
algorithm by including the magnetic field with the electric field in the formulation [76].
The combination includes more backscatter information and is assumed to provide
a better-quality image. To do so, we have chosen to combine the two fields into a
Poynting like vector that represents the magnitude and direction of power flow within
the system. At any point in Cartesian space, the Poynting vector is given by
x̂
ŷ
ẑ P = E × H = Ex Ey Ez Hx Hy Hz =(Ey Hz − Ez Hy )x̂ + (Ez Hx − Ex Hz )ŷ + (Ex Hy − Ey Hx )ẑ
=Px · x̂ + Py · ŷ + Pz · ẑ
(4.1)
where Px = Ey Hz − Ez Hy , Py = Ez Hx − Ex Hz , and Pz = Ex Hy − Ey Hx . All the
electric and magnetic field signals are assumed have been time shifted within this
section, except where otherwise noted.
62
At any instant in time, a single antenna operates in transmit mode and many
other antennas receive the response to that transmitted signal. Suppose that the
transmitted electric field is polarized along the y-direction, then Px and Pz at the receivers are likely to provide meaningful results. If the z-direction is assumed to point
away from the body, then Pz contains reflections from the tumor as well as significant
reflections from the muscle layer beneath the breast (see the breast model used in
Chapters 2 and 3). So for the proposed algorithm, Px is the only term that can be
reasonably used to detect the tumor with y-polarized excitation. Further, the first
term of Px dominates over the second term, again because of the polarization of excitation. Thus the x-component of the received Poynting vector is well-approximated
with
Px ≈ Ey Hz
(4.2)
From a similar line of reasoning, an x-polarized excitation signal gives rise to Py that
is well approximated with
Py ≈ −Ex Hz
(4.3)
It has been shown that the phase front of Hz is spherically antisymmetric about
the vertical (Y−Z) plane, in which the tumor is located. Therefore, Eqn (4.2) at
receiver locations in the positive x direction from the tumor is positive for all time;
Eqn (4.2) at receiver locations in the negative x direction from the tumor is negative
for all time. This problem is depicted in Figure 4.8 for a receiver in the positive and
negative x-direction from a tumor. Since a negative result represents power flow in the
negative direction, the total power reflected from a given focal point can be deduced
if each receiver in the negative x-direction of the current focal point is multiplied by
-1, or
Pxij =



Eyi Hzj
xR > x F


−Eyi Hzj
xR < x F
(4.4)
63
(a)
(b)
Figure 4.8: The product of Ey field and Hz field at a given receiver may be either (a)
exclusively positive, if the receiver is located in the positive x-direction from a tumor,
or (b) exclusively negative if the receiver is located in the negative x-direction from
a tumor.
where i, j = 1 − N (N is the total number of receivers), xR is the x coordinate of
the j th receiver and xF is the x coordinate of the current focal point. At this stage,
it is worth noting that the position of each detector has been taken into accout in
the proposed algorithm and participates in the intensity computation of each focal
point (for DAS, IDAS or DMAS, only the transit time of the signal from the focal
point to the detector is considered). Hence, when the x coordinate of the detector
lies between the tumor and the current focal-point, the x-directed power obtained
from these detectors are entirely inverted. This will further suppress the background
noise of the image and improve image quality. Finally, these time-shifted products
are summed to yield the power intensity value of the focal point according to
64
→
I (−
r)=
Z
T
r
r
r
(Ey1 Hz1
+ Ey1 Hz2
+ · · · + Ey1 HzN
0
r
r
r
+ Ey2 Hz1
+ Ey2 Hz2
+ · · · + Ey2 HzN
.
+ ..
r
r
r
+EzN Hz1
+ EyN Hz2
+ · · · + EyN HzN
) dt
#
Z T "X
N
N
X
r
=
Eyi ·
Hzj dt
0
Z
=
0
T
i=1
j=1
N
X
!
Pxij
dt
(4.5)
i,j=1
→
r
where −
r is the position of the synthetic focal point in 3D Cartesian space, Hzj
= Hzj
r
when xR > xF , Hzj
= −Hzj when xR < xF , and T is the total measurement time.
Note that Pxii is the actual Poynting vector associated with the ith pixel location;
Pxij (i 6= j) is a fictitious cross term that contributes to the focal point intensity but
has no physical meaning.
Eqn (4.5) clearly illustrates that the number of E field and H field detectors need
not be identical, nor must they be collocated. If M is the number of E field detectors
and N is the number of H field detectors, then the power intensity is given by
→
I (−
r)=
Z
0
T
"
M
X
Eyi ·
i=1
N
X
#
r
Hzj
dt
(4.6)
j=1
Thus Ey and Hz may be processed separately. This observation eases the burden
of collecting these field components in a physical measurement since each detecting
antenna may be optimized to detect either Ey or Hz , but not necessarily both.
To conclude the formulation, the power intensity of an x-polarized electric excitation signal from Eqn (4.3) is computed as
65
→
I(−
r)=
0
"
−
=
M
X
0
r
where, Hzj
=



H
zj


−Hzj
yR > y F
Pyij
dt
i,j=1
T
Z
!
N
X
T
Z
N
X
Exi ·
i=1
#
r
Hzj
dt
(4.7)
j=1
.
yR < y F
Next, consider a multi-polarized antenna strategy that is discussed in Chapter 3.
Since an x-direction polarized transmitter yields an −Ex Hz term, which is essentially
the Py that represents the microwave power flow in the y-direction, the power intensity
value of the synthetic focal point should be the summation of values obtained by Eqns
(4.5) and (4.7) from 4 illuminations [77],
−
I (→
r)=
Z
0
N
X
T
N
X
ij
Py1
i,j=1
i,j=1
+
N
X
ij
Px1
+
ij
Px2
i,j=1
+
N
X
!
ij
Py2
dt
(4.8)
i,j=1
Note that the value calculated by Eqn (4.8) is likely negative. When this happens all
intensity values are normalized to lie between zero and one.
4.3 The MRI-derived breast phantom and FDTD simulation
An MRI-derived breast phantom is naturally more realistic, but more complex
than a simplified breast model. To evaluate the MPI algorithm, three dimensional
dielectric and conductivity datasets from MRI measurement are applied in this chapter; these datasets were obtained from the University of Wisconsin MRI numerical
breast phantoms repository (UWCEM) [78].
66
(a)
(b)
Figure 4.9: Distribution of the dielectric properties in a sagittal slice from a 3-D
MRI-derived breast phantom. (a) Dielectric constant, (b) conductivity
67
(a)
(b)
Figure 4.10: The number of cells corresponding to (a) dielectric constant, and (b)
conductivity, within the applied breast phantom.
The selected breast-phantom, including the coupling medium, contains 258×253×
251 grid cells and each cell is 0.5mm×0.5mm×0.5mm. Fig. 4.9 shows the distribution
of relative permittivity and the conductivity values within the breast phantom. The
dielectric properties of the coupling liquid is selected to be r = 9.2, σ = 0.
Fig. 4.10 displays a statistic for the number of voxels (equivalent to Yee cell in
FDTD) associated with the dielectric constant and the conductivity values within
the breast shown in Fig. 4.9 (these figures exclude the muscle, skin, or the coupling
medium). A voxel that contains more fatty tissue is assummed to have lower dielectric
properties, while a voxel that contains more fibroconnective/glandular tissue is assumed to have relatively higher dielectric properties. Fig. 4.10 demonstrates that voxels having relatively low dielectric properties dominate in the breast phantom, which
indicates that the content of fat is more than the content of fibroconnective/glandular
tissues. As stated in the instruction manual of the UWCEM Repository, this breast
phantom contains approximately 25 − 50% glandular tissue. Therefore, the effective
wave velocity in the breast is evaluated, and will be used in a variety of algorithms
(DAS, MPI and so on) discussed in this chapter.
68
Figure 4.11: 56 receivers lie along 7 circles - each has 8 receivers (receivers not illustrated in this figure). Four transmitters (in red arrows) illuminate the breast
successively from different positions, polarized along -x, +y, +x and -y respectively.
We assume that an effective wave speed can always be defined to work for a
certain range of the density level (4 density levels are classified in UWCEM Repository
according to the breast density), due to the robustness of the DAS-family algorithm
in near-field microwave imaging [75]. However, the more dense the breast, the lower
contrast is obtained in the reconstructed image.
A synthetic aperture array shown in Fig. 4.11, which has 7 layers and each layer
has 8 elements (along the black circles, specific elements are not shown in this figure),
is applied to detect a spherical breast tumor centered at (78,75,50) with 8mm diameter
in the breast phantom. These receivers are assumed to be on the surface of the skin,
which is about 1.5mm thick and clearly demarcates the breast tissue and the coupling
medium in Fig. 4.9. Four transmitters (marked by arrows in Fig. 4.11, this figure
69
only shows 3 of them) take turns sending out a UWB signal (a modulated Gaussian
pulse described in Eqn. (3.4), the spectrum of this signal is displayed in Fig. 3.7)
after a previous transmission and backscattered-signal collection.
To carry out the MPI algorithm, we need to calculate the Poynting vector at a
specific point in the 3-D space. Assuming that these points are located in the center
of each Yee cell (take Fig. 2.1 as reference), then, the electric field signal requires
four point averaging and the H field signal requires two-point averaging:
1
1
1
Ex = Ex i + , j, k + Ex i + , j + 1, k
4
2
2
1
1
+Ex i + , j, k + 1 + Ex i + , j + 1, k + 1
2
2
1
1
1
Ey = Ey i, j + , k + Ey i + 1, j + , k
4
2
2
1
1
+Ey i, j + , k + 1 + Ey i + 1, j + , k + 1
2
2
1
1
1
+ Ez i + 1, j, k +
Ez = Ez i, j, k +
4
2
2
1
1
+ Ez i, j + 1, k +
+Ez i + 1, j + 1, k +
2
2
1
1
1
1
1
Hx = Hx i, j + , k +
+ Hx i + 1, j + , k +
2
2
2
2
2
1
1
1
1
1
+ Hy i + , j + 1, k +
Hy = Hy i + , j, k +
2
2
2
2
2
1
1
1
1
1
Hz = Hz i + , j + , k + Hz i + , j + , k + 1
2
2
2
2
2
(4.9)
(4.10)
(4.11)
(4.12)
(4.13)
(4.14)
The computational space is again terminated with a second-order Liao absorbing
boundary. Since the grid size is reduced in the MRI-derived breast phantom, to make
the FDTD simulation stable, our computation time-step is readjusted to ∆t = 4/3ps
and the total number of computation steps are adjusted to 5000 steps. Four transmissions, with respective polarization, as well as the backscattered signals collected
70
by the antenna array, take approximately 6 hours by serial fortran code on state of
the art linux servers. Thus, a total of 4 × 56 (N=56, the number of receivers) series
of data are obtained from the 3-D FDTD simulation.
4.4 Imaging results using the MRI-derived breast phantom
4.4.1 Single tumor
The calibration step is conducted by subtracting a reference model which is ±10%
random variation in dielectric properties from the tumor-bearing model. The calibrated sginals are imported into our matlab-based multi-polarized-MPI beamformer
(parallel processing on multicore is involved) to yield a reconstructed image. A sliced
image for horizontal, coronal, or sagittal reconstruction takes approximately 3 minutes, which again shows the efficiency of the MPI algorithm.
Fig. 4.12 illustrates the reconstructed image obtained through the multi-polarized
MPI approach in (a) x-y plane, (b) z-y plane, and (c) z-x plane, in which the tumor is
located. The dark dot, which represents the tumor location, can be easily recognized
from these images. And its location is consistent with the actual location in the FDTD
simulation. The reconstructed images show a good contrast against the clutters
and the background. Fig. 4.13 and Fig. 4.14 illustrate the image obtained by
multi-polarized DMAS and multi-polarized DAS respectively, with the same antenna
strategy, to compare with the result obtained by MPI. In the DMAS algorithm, the
backscattered signals received from the numerical breast phantom are time shifted,
multiplied in pair, and the products are summed to form a synthetic focal point [39].
Fig. 4.13 has a large number of clutters, while Fig. 4.14 has very poor image contrast.
To quantify further the performance of the three approaches, quantitative assessments are carried out in terms of signal-to-clutter ratio (SCR) and signal-to-mean
ratio (SM R). SCR compares the maximum tumor response with the maximum
clutter response in the same image, whereas SM R compares the maximum tumor
response with the mean response of the same image. The SCR and SM R for Fig.
71
4.12 − 4.14 are shown in Table 4.1. For all views, the SM R and SCR values for MPI
is the highest among the three.
(a)
(b)
(c)
Figure 4.12: The reconstructed image through multi-polarized MPI method. (a)
horizontal plane z=50; (b) coronal plane x=78; (c) sagittal plane y=75.
72
(a)
(b)
(c)
Figure 4.13: The reconstructed image through multi-polarized DMAS method. (a)
horizontal plane z=50; (b) coronal plane x=78; (c) sagittal plane y=75.
73
(a)
(b)
(c)
Figure 4.14: The reconstructed image through multi-polarized DAS method. (a)
horizontal plane z=50; (b) coronal plane x=78; (c) sagittal plane y=75.
74
Table 4.1: Comparison of SM R and SCR value for Fig. 4.12, 4.13, and 4.14
Fig number
Fig 4.12(a)
Fig 4.13(a)
Fig 4.14(a)
Fig 4.12(b)
Fig 4.13(b)
Fig 4.14(b)
Fig 4.12(c)
Fig 4.13(c)
Fig 4.14(c)
Method SM R (dB) SCR (dB)
MPI
5.311
2.001
DMAS
3.936
1.098
DAS
1.919
0.999
MPI
5.343
3.280
DMAS
4.187
0.067
DAS
2.375
1.650
MPI
5.680
1.768
DMAS
4.435
1.327
DAS
2.099
0.285
Considering that a relatively long interval from previous tomography examination
may occur, the reference breast phantom for the data calibration step may have considerable differences from the tumor-contained phantom. Fig. 4.15 shows the reconstructed image using multi-polarized MPI when the tumor-contained breast phantom
and the tumor-free breast phantom have ±15% difference in dielectric parameters.
The image quality is significantly degraded over the ±10% case though the tumor is
still able to be recognized centered at (78,45,60). Neither DAS nor DMAS is able to
locate the target in this scenario.
4.4.2 Dual tumor
Again, the dual tumor case is investigated to study the imaging resolution in
this numerical breast phantom, via the MPI algorithm. In this section, we study
both the horizontal and vertical imaging resolution of the multi-polarized MPI as an
illustration of the detection and imaging of a spherical pair of tumors, each of 6mm
diameter, using the MRI-derived breast phantom and the antenna scheme depicted in
Fig. 4.11 . The breast phantoms with and without tumors differ with ±10% random
variation.
75
(a)
(b)
(c)
Figure 4.15: Reconstructed image for tumor centered at (78,45,60) with 8mm diameter using MPI. Tumor-contained breast phantom and the tumor-free breast phantom
are ±15% randomly different in dielectric parameters. (a) in the x-y plane, (b) in the
z-y plane, and (c) in the z-x plane.
The horizontal plane resolution is determined by analyzing the distance between
two identical targets placed in the center of the x-y plane. Fig. 4.16(a) shows the
76
(a)
(b)
Figure 4.16: Two-tumor prototype for study of horizontal resolution. (a) is the reconstructed image when two tumors are 12mm apart. Center positions are (56,70,50)
and (68,70,50). (b) shows the intensity along the line y=70 in the plane of z=50 when
two tumors are 11mm, 12mm, and 13mm apart.
reconstructed image when two targets are centered at (56,70,50) and (68,70,50), a
12mm offset. It is difficult to identify the tumors in this image since clutters are very
strong. Fig.4.16(b) illustrates the intensity of the focal points along the line y=70 in
the reconstructed plane when two tumors are 11mm, 12mm, or 13mm apart. This
figure demonstrates that the resolution of the proposed reconstruction approach is
near 12mm in the horizontal plane. This translates to approximately 0.7λ at the peakspectrum frequency, and approximately 0.112λ at the lower edge frequency (1GHz)
of the UWB signal in the coupling medium. Fig. 4.16 also implies that clutters in
the 11mm-apart case (black dash line) would be even stronger than the 12mm-apart
and 13mm-apart case.
Similarly, the vertical resolution is studied by varying the distance between two
identical tumors, placed in the z-y plane. Fig. 4.17(a) shows the reconstructed image
when two 6mm-diameter tumors centered at (75,75,59) and (75,75,63). Hence the
center distance is 14mm. Clutters are observed to be strong in the image, however
two tumors can be clearly identified and distinguished. Fig.4.17(b) illustrates the
77
(a)
(b)
Figure 4.17: Two-tumor prototype for study of vertical resolution. (a) is the reconstructed image when two tumors are 14mm apart. Center positions are (75,75,59)
and (75,75,73). (b) shows the intensity along the line y=75 in the z-y plane of x=75
when two tumors are 13mm, and 14mm apart.
intensity of the focal points along the line y=75 in the reconstructed plane when two
tumors are 13mm or 14mm apart. This figure demonstrates that at a distance of
14mm, two tumors are able to be distinguished. This is equivalent to approximately
0.82λ at the peak-spectrum frequency of the UWB detecting signal and 0.131λ at
1GHz in the coupling medium.
CHAPTER 5: UWB ANTENNA DESIGN AND FABRICATION
A UWB system must have UWB antennas, which plays an important role in
broad band communication and imaging systems. The antenna desired for microwave
medical imaging, especially breast cancer imaging, must satisfy strict requirements of
bandwidth, pulse waveform, radiation pattern, and antenna size (small antennas are
prefered). The reasons are: for real synthetic aperture arrays, there must be several
antennas around the breast with reasonable spacings; For non-real aperture arrays,
an antenna is moved accross the breast to collect signals at several location, while
big antennas may have accuracy problem in moving, which subsequently affects the
quality of beamforming. Therefore, a large number of investigations in small UWB
antennas have been carried out to design medical-application-aimed antennas.
In 2003, Li et. al. proposed a pyramidal horn antenna for breast cancer detection,
which works in the 1-11 GHz spectrum [79]. In Li’s experiment, this antenna serves
as a transmitter and a receiver. It is moved across the breast to create a synthetic
aperture array. As compared to the antenna built by Li, microstrip antennas have
a more compact “2-D” form, but ususally with narrow bandwidth. However, the
Vivaldi antenna [80] has recently received considerable attention because of its “2D” structure and broad band property. The antipodal Vivaldi antenna, having an
even simpler structure than conventional Vivaldi which makes it easy to design and
fabricate, has been employed in many communication applications [81]− [91].
However, only a few studies about medical-imaging-aimed microstrip antennas
(Vivaldi or other types [92] [93]) have been reported. Among them, it is worth noting
Bourqui et. al.’s work [94], in which a balanced antipodal Vivaldi was built for breast
cancer detection, accompanied with a director ahead of the antenna. The structure
79
of this antenna was coded in FDTD to simulate the whole process of transmitting
and receiving signals from a breast phantom. Good imaging results were obtained
using data collected by this antenna through FDTD simulation. The problem in this
design, is that the antenna is still quite large (80 × 44 × 9.2mm, with the director
included). It is impossible to use this antenna to build a real synthetic aperture array
for microwave breast cancer imaging because of its size. Therefore, in this chapter, we
propose two antipodal Vivaldi antennas - each quite small - and finally fabricate an
antenna array using one of which, for the application of breast cancer detection [95].
5.1 Two antipolar Vivaldi antennas
The proposed Vivaldi antennas have exponential structures. Fig. 5.1 and Fig. 5.2
present the geometry and parameter values of the proposed Vivaldi antennas. The
curvatures of the outer curve and the inner curve (inner curves form the gap) follow
the equation
xin = c1 ec2 (y−L−C) − 1 − W
y−L−C
xout = W 2e 6 − 1
(5.1)
(5.2)
where W, L, and C are shown in Fig. 5.1(a) and Fig. 5.2(a). The coefficients c1 and
c2 for our antennas are listed in Table 5.1.
Table 5.1: The coefficients used in Eqn 5.1.
Coeffficient
c1
c2
Antenna #1 in Fig. 5.1
0.25
0.15
Antenna #2 in Fig. 5.2
0.06
0.2
80
(a)
(b)
Figure 5.1: Geometry and parameters of a proposed Vivaldi antenna (Antenna #1)
copper pattern. (a) top view, (b) 3-D view. Unit : mm.
81
(a)
(b)
Figure 5.2: Geometry and parameters of a second Vivaldi antenna (Antenna #2)
copper pattern. (a) top view, (b) 3-D view. Unit: mm.
82
(a)
(b)
Figure 5.3: The constructed Vivaldi antenna #1. (a) top view, (b) bottom view.
(a)
(b)
Figure 5.4: The constructed Vivaldi antenna #2. (a) top view, (b) bottom view.
In both designs, the copper layer is terminated with a tilted half disc, to reduce the
reflection from the end. The antenna is assumed to be fed through an SMA connector
followed by a gradual transition from microstrip to parallel strips transmission line.
Along the transition, the conductor width increases linearly while the ground width
decreases exponentially to retain a constant impedance. The tri-strip-like transmission line extends for a short distance before the grounds and conductor start to flare
in opposite directions with exponential curves to create the antenna aperture.
83
(a)
(b)
Figure 5.5: The 2-D Electric field of Antenna #1 in the x-y plane when the frequency
is (a) 6 GHz, (b) 10 GHz.
The substrate is constructed using TMM 10 (Rogers Corporation) that has a
relative permittivity of 9.2, and thickness 1.27 mm. The second antenna (Fig. 5.2)
has a longer feeding leg and a reduced gap, compared to Antenna #1 (Fig. 5.1). The
overall sizes of Antenna #1 and Antenna #2 are 29 × 32 × 1.27mm, and 33 × 32 ×
1.27mm, respectively. The constructed antennas are shown in Fig. 5.3 and 5.4.
The antennas used for breast cancer imaging, as disscussed in the previous chapter,
are assumed to be immersed in a coupling medium when operating in both transmit
and receive mode. Fig. 5.5 shows the HFSS-simulated Electric field on the x-y
plane, generated by Antenna #1 when it is centered in the x-y plane, fed from the
origin, and immersed in an ideal coupling liquid with relative permittivity of 9.2. The
image indicates a good radiation in the near field by this antenna. In the antenna
measurement, we used a commercial automotive fuel named E85 as the coupling
liquid, which contains 85% ethonal, plus gasoline. Fig. 5.6 shows the HFSS-simulated
and measured reflection coefficient S11 over the freuqency span 1 GHz − 10 GHz.
The measurement matches the main features of the simulation especially in the low
frequency range. The main source of error comes from the real dieletric properties of
the coupling liquid do not quite match the parameters used in our simulation.
84
Figure 5.6: The measured (solid line) and HFSS-simulated (dashed line) S11 for
antenna #1.
Figure 5.7: The measured (solid line) and HFSS-simulated (dashed line) S11 for
antenna #2.
85
(a)
(b)
Figure 5.8: The 2-D Electric field of Antenna #2 in the x-y plane when the frequency
is (a) 6 GHz, (b) 10 GHz.
The same measurement is conducted for Antenna #2. We find that the field
generated by Antenna #2 does not present significantly different fields than those
generated by Antenna # 1, from HFSS simulation. Its measured and simulated S11
are illustrated in Fig. 5.7.
5.2 The antenna array
We selected Antenna #1 as the antenna element employed in the antenna array
because it is relatively small. As shown in Fig. 5.9, eight antennas were built on one
substrate and have the same polarization. This arrangement makes the antennas easy
to feed. The hole in the middle allowing for measurement of a breast, is 100mm in
diameter. By moving the array up and down in the z-direction, the array can collect
the siganl from the breast at different heights, shown in Fig. 5.10 (7 levels are applied
in the method discussed in Chapter 4), to conform to synthetic aperture imaging.
Since the array aperture is fixed, the antennas are comparatively far from the surface
of the breast as they move up, if the breast is still assumed to be hemispherical.
86
Figure 5.9: The designed antenna array in HFSS. Unit: mm.
Figure 5.10: The designed antenna array for breast cancer imaging.
87
The constructed antenna array with the SMA connector is shown in Fig. 5.11.
The multi-polarized approach, discussed in the previous chapter, can be conducted by
turning the array 90 degrees - then all the antennas would have vertical polarization
corresponding to their former polarization. The overall size of the array is 152.4 ×
152.4 × 1.27mm. The material of the substrate still is Rogers TMM 10 (relatively
permittivity 9.2).
88
(a)
(b)
Figure 5.11: The constructed antenna array.(a) top view, (b) bottom view.
CHAPTER 6: CONCLUSIONS
This dissertation presents a study of an ultrawideband microwave imaging system
for the detection of ealy-stage breast cancer. Our ongoing work in this research is
motivated by the clinical need for a viable complement to, or replacement for X-ray
mammography, which suffers from a number of disadvantages. The physical basis for
breast cancer detection via microwave method is the contrast in dielectric properties
of healthy and malignant breast tissues. In our investigated multi-polarized detection
system, an ultrawideband signal is transmitted by vertically-polarized linear sources
sequentially, and an array of antennas is located near the surface of the breast to
collect backscattered signals from the breast. The received signals are processed using
artifact removal and MPI beamforming algorithms to form an image of backscatter
energy in the image due to their significant dielectric property contrast with normal
breast tissue. Applying our detection system to an MRI-derived numerical breast
phantom, we find that the proposed screening scheme and the algorithm are able to
detect cancerous growth within the breast effectively and provide accurate location
of the suspicious object.
As the first step to construct a real detection system to carry out our scheme for
breast cancer microwave detection, we fabricated two small Vivaldi antennas and a
real aperture antenna array composed of 8 Vivaldi antennas. The designed antipodal
antennas are designed to work in the frequency range of 1 GHz to 10 GHz. Our
simulation and measurement results show that this antenna is able to transmit and
receive signals in the desired frequency range under the experimental enviroment.
Our next work will be focused on the hardware construction of the system, including
signal amplification and filtering, circuit connection, etc.
90
To summarize, the medical microwave imaging research continuing in our laboratory is encouraging. We believe that we are close to a real breast cancer detection
system. We also believe that the microwave method will finally be a reliable approach for early-stage breast cancer examination, through the efforts of hundreds of
researchers in the microwave imaging community.
91
BIBLIOGRAPHY
[1] A. Jemal, T. Murray, E. Ward, et al. “Cancer statistics, 2005,” CA Cancer J.
Clin., Vol. 55, No. 1, pp. 10-30, Jan-Feb 2005.
[2] American Cancer Society, Breast Cancer Facts Figures 2011-2012, 2011. Available on www.cancer.org/Research/Cancer
FactsFigures/BreastCacerFactsFigures.
[3] “U.S. Breast Cancer Statistics,” Available on www.breascancer.org, Oct. 2011.
[4] P. T. Huynh, A. M. Jarolimek, and S. Daye, “The false-negative mammogram,”
Radiograph., Vol.18, No. 5, pp.1137-1154, 1998.
[5] J.G. Elmore, M. B. Barton, V. M. Moceri, S. Polk, P. J. Arena, and S. W.
Fletcher, “Ten-year risk of false positive screening mammagrams and clinical
breast examinations,” New Eng. J. Med., Vol.338, No.16, pp.1089-1096, 1998.
[6] M. Kriege, Cecile T.M. Brekelmans, C. Boetes, P.E. Besnard, H.M. Zonderland, I.M. Obdeijn, R.A. Manoliu, T. Kok, H. Peterse, Madeleine M.A. TilanusLinthorst, S.H. Muller, S. Meijer, J.C. Oosterwijk, Louk V.A.M. Beex, Rob
A.E.M. Tollenaar, H.J. de Koning, Emiel J.T. Rutgers, and Jan G.M. Klijn,
“Efficacy of MRI and Mammography for Breast-Cancer Screening in Women
with a Familial or Genetic Predisposition,” New Eng. J. Med., Vol.351, No.5,
pp.427-437, 2004.
[7] S.S. Chaudhary, R.K. Mishra, A. Swarup, and J.M. Thomas, “Dielectric properties of normal and malignant human breast tissues at radiowave and microwave
frequencies,” Indian J. Biochem. Biiophys., Vol. 21, pp. 76-79, 1984.
[8] A.J. Surowiec, S.S. Stuchly, J.R. Barr, and A. Swarup, “Dielectric properties of
breast carcinoma and the surrounding tissues,” IEEE Trans. Biomed. Eng., Vol.
35, pp. 257-263, Apr. 1988.
[9] W.T. Joies, Y.Z. Dhenxing, and R.L. Jirtle. “The measured electrical properties
of normal and malignant human tissues from 50 to 900 MHz,” Med. Phy., Vol.
21, pp. 547-550, 1994.
[10] X. Li and S.C. Hagness, “A confocal microwave imaging algorithm for breast
cancer detection,” IEEE Microwave Wireless Components Lett, Vol. 11, pp.130132, Mar. 2001.
92
[11] M. Lazebnik, C. Zhu, G.M. Palmer, J. Harter, S. Sewall, N. Ramanujam, and
S.C. Hagness, “Tissue specimens obtained from reduction surgeries: comparison
of optical and microwave properties,” IEEE Trans. Biomed. Eng., Vol. 55, No.10,
pp. 2444-2451, Oct. 2008.
[12] K.S. Cole, R.H. Cole, “Dispersion and Absorption in Dielectrics - I Alternating
current characteristics,” J. Chem. Phys., Vol. 9, pp. 341352, 1941.
[13] K.S. Cole, R.H. Cole, “Dispersion and Absorption in Dielectrics - II Direct current characteristics”. J. Chem. Phys., Vol. 10, pp. 98105, 1942.
[14] M. Lazebnik, D. Popovic, L. McCartney, C. B. Watkins, M. J. Lindstrom,
J.Harter, S. Sewall, T. Ogilvie, A. Magliocco, T. M. Breslin, W. Temple, D.
Mew, J. H. Booske, M. Okoniewski, and S. C. Hagness, “A large-scale study of
the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries,” Phys. Med. Biol., Vol. 52,
pp. 2637-2656, 2007.
[15] M. Lazebnik, E.L. Madsen, G.R. Frank, and S.C. Hagness, “Tissue-mimicking
phantom materials for narrowband and ultrawideband microwave applications,”
Phys. Med. Biol., Vol. 50, pp. 4245-4258, 2005.
[16] D. Byrne, M. O’Halloran, M. Glavin, and E. Jones, “Contrast enhanced beamforming for breast cancer detection,” Pier, Vol. 28, pp. 219-234, 2011.
[17] A. Mashal, B. Sitharaman, L. Xu, P.K. Avti, A.V. Sahakian, J.H. Booske, S.C.
Hagness, “Toward carbon-nanotube-based theranostic agents for microwave detection and treatment of breast cancer: enhanced dielectric and heating response
of tissue-mimicking materials,” IEEE. Trans. Biomed. Eng., Vol. 57, No. 8, pp.
1831-1834.
[18] J. D. Shea, P. Kosmas, S. C. Hagness, and B. D. Van Veen, “Contrast-enhanced
microwave imaging of breast tumors: A computational study using 3D realistic
numerical phantoms,” Inverse Problems, Vol. 26, 074009 (22 pages), 2010.
[19] R.A. Kruger, K.D. Miller, H.E. Reynolds,Jr WL Kiser, D.R. Reinecke, G.A.
Kruger, “Contrast enhancement of breast cancer in vivo using thermoacoustic
CT at 434 MHz,” Radiology, Vol. 216, pp. 279-283, 2000.
[20] B. Guo, J. Li, H. Zmuda, M. Sheplak, “Multifrequency microwave-induced thermal acoustic imaging for breast cancer detection,” IEEE Trans. Biomed. Eng.,
Vol. 54, No. 11, Nov. 2007.
[21] Yao Xie, Bin Guo, Jian Li, Geng Ku, L.V. Wang, “Adaptive and Robust Methods
of Reconstruction (ARMOR) for Thermoacoustic Tomography,” IEEE Trans.
Biomed. Eng., Vol. 55, No. 12, pp. 2741-2752.
93
[22] L. Nie, D. Xing, Q. Zhou, D. Yang, H. Guo, “Microwave-induced thermoacoustic
scanning CT for high-contrast and noninvasive breast cancer imaging,” Med.
Phy., Vo. 35, No. 9, pp. 4026-4032, 2008.
[23] A. Mashal, J.H. Booske, and S.C. Hagness, “Toward contrast-enhanced
microwave-induced thermoacoustic imaging of breast cancer: an experimental
study of the effects of microbubbles on simple thermoacoustic targets,” Phys.
Med. Biol., Vol. 54, pp. 641-650, 2009.
[24] X. Wang, H. Xin, D. Bauer, and R. Witte, “Microwave induced thermal acoustic imaging modeling for potential breast cancer detection,” IEEE APSURIS
Symposium 2011, pp. 722-725, 2011.
[25] G.K. Zhu, and M. Popovic, “Comparison of radar and thermoacoustic techniques
in microwave breast imaging,” Pier, Vol. 35, pp. 1-14, 2011.
[26] T. Cui and W. Chew, “Diffraction Tomographic Algorithm for the Detection of
Three-Dimensional Objects Buried in a Lossy Half-Space,” IEEE Trans. Ant.
Prop., vol.50, No. 1, pp. 42-49, Jan. 2002.
[27] M. Born and E. Wolf, Principles of Optics, 6th ed. New York: Pergamon Press,
1980.
[28] M. Moghaddam, W. C. Chew, and M. Oristaglio,“Comparison of the Born iterative method and Tarantolas method for an electromagnetic time-domain inverse
problem,” Int. J. Imag. Sys. Tech., vol. 3, no. 4, pp. 318333, 1991.
[29] W. Chew and Y. Wang, “Reconstruction of two-dimensional permittivity distribution using the distorted Born iterative method,” IEEE Trans. Med. Imaging,
vol. 9, No. 2, pp. 218-225, Jun. 1990.
[30] D. W. Winters, J. D. Shea, P. Kosmas, B. D. Van Veen, and S. C. Hagness,
“Three-dimensional microwave breast imaging: Dispersive dielectric properties
estimation using patient-specific basis functions,” IEEE Trans. Med. Imaging,
vol. 28, No. 7, pp. 969-981, July 2009.
[31] H. Zhang, S.Y. Tan, and H.S. Tan, “A novel method for microwave breast cancer
detection,” Pier, Vol. 83, pp. 413-434, 2008.
[32] N. Irishina, M. Moscoso, and O. Dorn, “Microwave tomography for breast cancer
detection using level sets,” Proceeding 23rd ACES, pp. 1955-1960.
[33] D. W. Winters, E. J. Bond, B. D. Van Veen, and S. C. Hagness, “Estimation
of the frequency-dependent average dielectric properties of breast tissue using
a time-domain inverse scattering technique,” IEEE Trans. Ante. Prop. Vol. 54,
No. 11, pp. 3517-3528, Nov. 2006.
94
[34] D. W. Winters, B. D. Van Veen, and S. C. Hagness, “A sparsity regularization
approach to the electromagnetic inverse scattering problem,” IEEE Trans. Ante.
Prop., Vol. 58, No. 1, pp. 145-154, January 2010.
[35] J. D. Shea, P. Kosmas, S. C. Hagness, and B. D. Van Veen, “Three-dimensional
microwave imaging of realistic numerical breast phantoms via a multiplefrequency inverse scattering technique,” Medical Physics, Vol. 37, No. 8, pp.
4210-4226, August 2010.
[36] E.C. Fear, S.C. Hagness, P. M. Meaney, M. Okoniewski, M.A. Stuchly, “Enhancing Breast tumor detection with near-field imaging,” IEEE. microwave Mag.,
pp.48-56, Mar. 2002.
[37] E.C. Fear, X. Li, S.C. Hagness, and M.A. Stuchly, “Confocal microwave imaging
for breast cancer detection: localization of tumors in three dimensions,” IEEE
Trans. Biomed. Eng., Vol. 49, No. 8, pp. 812-822, Aug. 2002.
[38] M. Klemm, I. J. Craddock, J. A. Leendertz, A. Preece, and R. Benjamin,
“Improved delay-and-sum beamforming algorithm for breast cancer detection,”
Int. Journal of Ant. and Prop., Vol. 2008, Article ID 761402, 9 pages, 2008.
doi:10.1155/2008/761402
[39] H. B. Lim, N.T. T. Nhung, E. Li and N.D. Thang, “Confocal Microwave imaging for breast cancer detection: delay-multiply-and-sum image reconstruction
algorithm,” IEEE Trans. Biomed. Eng., Vol.55, No. 6, pp. 1697-1704, 2008.
[40] M. Converse, E.J. Bond, S.C. Hagness, B.D. Van Veen, “Ultrawide-band microwave space-time beamforming for hyperthermia treatment of breast cancer: a
computational feasibility study,” IEEE. Trans. Micr. Theo. Tech., Vol. 52, No.
8, pp. 1876-1889, 2004,
[41] X. Li, S.K. Davis, S.C. Hagness, D.W. Van der Weide, B.D. Van Veen, “Microwave imaging via space-time beamforming: experimental investigation of tumor detection in multilayer breast phantoms,” IEEE Trans. Micro. Theo. Tech.,
Vo. 52, No. 8, pp. 1856-1865, 2004.
[42] E. J. Bond, X. Li, S.C. Hagness, B.D. Van Veen, “ Microwave imaging via spacetime beamforming for early detection of breast cancer,” IEEE Trans. Ant. Prop.,
Vol. 51, No. 8, pp. 1690-1705, 2003.
[43] B.Liu, X. Xiao, X. Liu, “Ultra-wideband microwave image reconstruction by
robust capon beamforming algorithm for early breast cancer detection,” Proc.
control, automation and system engineering 2011, pp. 1-4, July, 2011.
[44] P. Kosmas, C. Rappaport, “FDTD-Based time reversal for microwave breast
cancer detection - localization in three dimensions,” IEEE Trans. Microwave
theory tech., Vol. 54, No. 4, pp. 1921-1927, 2006.
95
[45] Y. Jin, Y. Jiang, J.M.F. Moura, “Time reversal beamforming for microwave
breast cancer detection,” Proc. IEEE Image Processing, Vol. 5, pp. V13-V16,
2007.
[46] P. Kosmas, C. Rappaport, “A matched-filter FDTD-based time reversal approach for microwave breast cancer detection,” IEEE Trans. Ant. Prop., Vol.
54, No.4, pp. 1257-1264.
[47] S. K. Davis, H. Tandradinata, S. C. Hagness, and B. D. Van Veen, “Ultrawideband microwave breast cancer detection: A detection-theoretic approach using
the generalized likelihood ratio test,” IEEE Trans. Biomed. Eng., Vol. 52, No.
7, pp. 1237-1250, July 2005.
[48] B. Guo, Y. Wang, J. Li, P. Stoica, and R. Wu, “Microwave imaging via adaptive
beamforming methods for breast cancer detection,” J. Electromagnetic Waves
and Applications, Vol. 20, No. 1, pp. 53-63, 2006.
[49] Y. Xie, B. Guo, J. Li, and P. Stoica, “Novel Multi-static Adaptive Microwave
Imaging Methods for Early Breast Cancer Detection,” EURASIP Journal on
Applied Signal Processing, no. 91961, pp. 1-13, Sep. 2006.
[50] Y. Xie, B. Guo, L. Xu, J. Li, “Multistatic adaptive microwave imaging for early
breast cancer detection,” IEEE Trans. Biomed. Eng., Vol. 53, No. 8, pp.16471657, Aug. 2006.
[51] K. Yee, “Numerical solution of initial boundary value problems involving
Maxwell’s equations in isotropic media,” IEEE Trans. Ant. Prop., Vol.14, No. 3,
pp. 302307, 1966.
[52] A. Taflove, “Application of the finite-difference time-domain method to sinusoidal steady state electromagnetic penetration problems,” IEEE Trans. Electromagnetic Compatibility, Vol. 22, No. 3, pp. 191202, 1980.
[53] S. C. Hagness, A. Taflove, and J. E. Bridges, “Two-dimensional FDTD analysis
of a pulsed microwave confocal system for breast cancer detection: Fixed-focus
and antenna-array sensors,” IEEE Trans. Biomed. Eng., Vol. 45, Vol. 12, pp.
14701479, 1998.
[54] A. Taflove, and S. C. Hagness, Computational Electrodynamics: The FiniteDifference Time-Domain Method, 3rd ed., Artech House Publishers, ISBN 158053-832-0, 2005.
[55] Matthew N. O. Sadiku, Numerical techniques in electromagnetics, 2nd ed., CRC
Press LLC, 2001.
[56] Mur G, “Absorbing boundary conditions for the finite-difference approximation
of the time-domain electromagnetic field equations,” IEEE Trans. Electromagn.
Compat., Vol. 23, No. 4, pp. 377-382, Nov. 1981.
96
[57] P. Liao, H. L. Wong, B. P. Yang, and Y. F. Yuan, “A transmitting boundary
for transient wave analysis,” Scientia Sinica, Vol. 27, No. 10, pp. 1065, October
1984.
[58] M. Moghaddam, W. C. Chew, “Stabilizing Liao’s absorbing boundary conditions
using single-precision arithmetic,” Proceeding of APS1991, Vol.1, pp. 430-433,
1991.
[59] J.C. Olivier, “On the synthesis of exact free space absorbing boundary conditions
for the finite-difference time-domain method,” IEEE Trans. Ant. Prog., Vol. 40,
No. 4, pp. 456460, April 1992.
[60] J.P. Berenger, “A perfectly matched layer for the absorption of electromagnetic
waves,” Jour. Comp. Phys., Vol. 114, pp. 185200, Aug. 1994.
[61] J.P. Berenger, “Perfectly matched layer for the FDTD solution of wavestructure
interaction problems,” IEEE Trans. Ant. Prop., Vol. 44, No. 1, pp. 110117, Jan.
1996.
[62] Z. S. Sacks, D. M. Kingsland, D. M. Lee, J. F. Lee, “A perfectly matched
anisotropic absorber for use as an absorbing boundary condition,” IEEE Trans.
Antennas Propagat., Vol. 43, No. 12, pp. 1460-1463, Dec. 1995.
[63] S. D. Gedney, “An anisotropic perfectly matched layer absorbing media for the
trunction of FDTD lattices,” IEEE Trans. Antennas Prop., Vol. 44, No. 12, pp.
1630-1639, Dec. 1996.
[64] S. D. Gedney, “An anisotropic PML absorbing media for the FDTD simulation
for fields in lossy and dispersive media,” Electromagnetics, Vol. 16, No. 4, pp.
399-415, 1996.
[65] I. W. Sudiarta, “An absorbing boundary condition for FDTD trunction using
multiple absorbing surfaces,” IEEE Trans. Antennas Propagat., Vol. 51, No. 2,
pp. 3268-3275, Dec. 2003.
[66] T. S. Yeo, P. S. Kooi, M. S. Leong, and R. H. Feng, “A performance assessment
of Liaos absorbing boundary conditions for FDTD method,” Microwave Optics
tech. lett., Vol. 16, No. 3, pp. 186-197, Oct. 20, 1997.
[67] M.N.O. Sadiku, V. Bemmel, and S. Agbo, “Stability criteria for finitedifference
time-domain algorithm,” Proc. IEEE Southeastcon, pp. 4850, April 1990.
[68] Daniel Flores-Tapia, G. Thomas and S. Pistorius, “Skin surface removal on breast
microwave imagery usng wavelet multiscale products,” Proceedings of the 5th
medical imaging: physiology, function and structure from medical images, 2006.
[69] University of Texas MD Anderson Cancer Center, “Breast Cancer Screening:
Increased Risk”, available on http://www.mdanderson.org.
97
[70] B. Zhou, W. Shao, and G. Wang, “UWB microwave imaging for early breast
cancer detection: Effect of the coupling medium on resolution,” Proc. Radio Sci.
Conf., pp. 431434, 2004.
[71] J. M. Sill and E. C. Fear, “Tissue sensing adaptive radar for breast cancer detection: Study of immersion liquid,” Electron. Lett., Vol. 41, No. 3, pp. 113115,
2005.
[72] W. Shao, R. S. Adams. “UWB imaging with multi-polarized signals for early
breast,” Proc. IEEE AP-S International Symposium 2010, pp. 1-4, July 2010.
[73] G. Brooker, “Sensors and signals,” Chapter 6, pp. 305, 2007.
[74] W. Shao, B. Zhou, G. Wang, “Early breast tumor imaging via UWB microwave
method: study on multi-target detection,” Proc. IEEE AP-S International Symposium 2005, Vol. 3A, pp. 835-838, 2005.
[75] G. Wang, X. Zeng, “Impact of dispersion in breast tissue on high-resolution
microwave imaging for early breast tumor detection,” Proc. IEEE AP-S International Symposium 2004, vol. 3, pp. 2452-2455, June 2004.
[76] W. Shao, R.S. Adams, “UWB microwave imaging for early breast cancer detection: A novel confocal imaging algorithm,” Proc. IEEE AP-S International
Symposium 2011, pp. 707-709, July 2011.
[77] W. Shao, R.S. Adams, “Multi-polarized microwave power imaging algorithm for
early breast cancer detection,” Pier M, Vol. 23, pp. 93-107, 2012.
[78] The UWCEM Numerical Breast Phantoms
http://uwcem.ece.wisc.edu/MRIdatabase/.
Repository.
Available
at
[79] X. Li, S.C. Hagness, M.K. Choi and D.W. van der Weide, “Numerical and experimental investigation of an ultrawideband ridged pyramidal horn antenna with
curved launching plane for pulse radiation,” IEEE Ante. Wireless Propag. Lett.,
Vol. 2 , pp. 259-262,2003.
[80] P.J. Gibson, “The vivaldi aerial,” Proc. 9th Europe microwave conference, pp.
101-105, 1979.
[81] A. Hood, T. Karacolak, E. Topsakal, “A small antipodal Vivaldi antenna for
ultrawide-band applications,” IEEE Ante. Wireless Prop. Lett., Vol. 7, pp. 656660, 2008.
[82] P. Fei, Y. Jiao, W. Hu, F. Zhang, “A miniaturized antipodal Vivaldi antenna
with improved radiation characteristics,” IEEE Ante. Wireless Prop. Lett., Vol.
10, pp. 127-130, 2011.
98
[83] J. Bai, S. Shi, D.W. Prather, “Modified compact antipodal vivaldi antenna for
450-GHz UWB application,” IEEE Trans. Micr. Theo. Tech, Vol. 59, No. 4, pp.
1051-1057, 2011.
[84] Y. Che; K. Li; X. Hou; W. Tian, “Simulation of a small sized antipodal Vivaldi
antenna for UWB applications,” Proc. ICUWB 2010, Vol. 1, pp. 1-3, 2010.
[85] S. Wang, X.D. Chen; C.G. Parini, “Analysis of ultra wideband antipodal vivaldi
antenna design,” Proc. Ante. Prop. 2007, pp. 129-132, 2007.
[86] S. Chamaani, S.A. Mirtaheri, M.S. Abrishamian, “Improvement of time and
frequency domain performance of antipodal vivaldi antenna using multi-objective
particle swarm optimization,” IEEE Trans. Ante. Prop., Vol. 59, No. 5, pp. 17381742, 2011.
[87] M. Chiappe, G.L. Gragnani, “Vivaldi antennas as detectors for microwave imaging: some theoretical results and design considerations,” Proc. Imaging Syst.
Tech 2004, pp. 22-27, 2004.
[88] M. Chiappe, G.L. Gragnani, “Vivaldi antennas as detectors for microwave imaging: further steps in its radiation features analysis,” Proc. Imaging Syst. Tech
2005, pp. 8-13, 2005.
[89] M. Chiappe, G.L. Gragnani, “Vivaldi antennas for microwave imaging: theoretical analysis and design considerations,” IEEE Trans. Inst. Meas., Vol. 55, No.
6, pp. 1885-1891, 2006.
[90] X. Zhuge, A. Yarovoy, “Design of low profile antipodal Vivaldi antenna for ultrawideband near-field imaging,” Proc. Ante. Prop. 4th European Conference, pp.
1-5, 2010.
[91] X. Zhuge, A. Yarovoy , L.P. Ligthart, “Circularly tapered antipodal Vivaldi
antenna for array-based ultra-wideband near-field imaging,” Proc. 6th European
Radar Conference, pp. 250-253, 2009.
[92] S.M. Salvador and G. Vecchi, “Experimental tests of microwave breast cancer
detection on phantoms,” IEEE Trans Ante. Prop., Vol. 57, No. 6, pp. 1705-1712,
2009.
[93] Y. Wang, M.R. Mahfouz, “Novel compact tapered microstrip slot antenna for
microwave breast imaging,” Proc. IEEE AP-S International Symposium 2011,
pp. 2119-2122, 2011.
[94] J. Bourqui, M.Okoniewski, E.C. Fear, “Balanced antipodal vivaldi antenna with
dielectric director for near-field microwave imaging,” IEEE Trans Ante. Prop.,
Vol. 58, No. 7, 2010.
99
[95] W. Shao, R. S. Adams, “A microstrip antenna array using Vivaldi antennas
for early breast cancer imaging,” IEEE Antennas Wireless Propag. Lett., to be
submitted.